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<s>Proceedings of the...S Bandyopadhyay, D S Sharma and R Sangal. Proc. of the 14th Intl. Conference on Natural Language Processing, pages 427–434,Kolkata, India. December 2017. c©2016 NLP Association of India (NLPAI)Study on Visual Word Recognition in Bangla across Different ReaderGroupsManjira SinhaConduent Labs IndiaBangalore, IndiaTirthankar DasguptaTCS Innovation LabsKolkata, India{manjira87, iamtirthankar, anupambas}@gmail.comAnupam BasuIIT KharagpurKharagpur, IndiaAbstractThis paper presents a psycholinguis-tic study of visual word recognition inBangla. The study examines the relation-ship among different word attributes andword reading behaviors of the two tar-get user groups, whose native language isBangla. The different target user groupsalso offer insights into the subjectivity ofwritten word comprehension based on thereaders background. For the purpose ofthe study, reading in terms of visual stimu-lus for word comprehension has been con-sidered. To the best of the knowledge ofthe authors, this study is the first of its kindfor a language like Bangla.1 IntroductionRecognition and understanding of words are ba-sic building blocks and the first step in languagecomprehension. At this stage, the form (visualrepresentation) joins the meaning (conceptual rep-resentation). Therefore, the cognitive load asso-ciated with word reading is a significant contrib-utor to the overall text readability. The presentstudy aims to capture the salience effects of dif-ferent word attributes on the word reading perfor-mance in Bangla, the second most spoken (afterHindi) and one of the official languages of Indiawith about 85 million native users in India 1 . Thefeatures studied in this work, encompass ortho-graphic properties of a word like length in termsof the number of visual units or akshars; numberof unique orthographic shapes i.e, the characteris-tic strokes and complexity measures based on thefamiliarity of the akshars and strokes in a word.Phonological properties of a word such as numberof syllables and spelling to sound consistency have1http://www.ethnologue.com/statistics/sizealso been taken into account along with the seman-tic attributes of a word like number of synonymsand number of senses. Moreover, the feature listalso includes word collocation attributes such asorthographic neighborhood size and phonologicalneighborhood size, which situate the given wordwith respect to other members in the vocabulary.The effects of the word attributes have been mea-sured in terms of the reaction time and perfor-mance accuracy data obtained from empirical userexperiments.The paper is organized as follows: Section 2presents the relevant literature study, section 3describes the participants details; section 4 and5 states data preparation and the psycholinguis-tic experiment respectively; section 6 presents thefeature descriptions and the experimental observa-tions against words and non-words; finally, section7 concludes the paper.2 Related WorksResearch in word recognition has been central tomany areas in cognitive-neuroscience (Frost et al.,2005), educational processes (Seidenberg, 2013),attention (Zevin and Balota, 2000), serial versusparallel processing (Coltheart et al., 1993), con-nectionism (Plaut et al., 1996) and much more.Typically, two different techniques are used tostudy visual word recognition: the lexical decisiontasks and the naming task (Balota et al., 2004). Inlexical decision task, a letter string is presentedto a participants are asked to decide whether thegiven string is a valid word in their language.On the other hand, in the naming task, partici-pants are asked to read allowed a letter string asquickly as possible. The time taken by a</s>
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<s>subjectto complete each task after the visual presentationof the target is defined as the response time (RT).An analysis of the reaction times of the subjects427reveals the actual processing of words in brain.The early works in word recognition involves twodistinct models: the activation model or the lo-gogen model (Morton, 1969) and the search model(Forster and Bednall, 1976); both of these twomodels are based on the fundamental premises ofthe frequency effects in word recognition. The fre-quency effect in word recognition claims that thehigh frequency words are recognized more accu-rately and quickly than the low-frequency words(Murray and Forster, 2004). The logogen modelassumes recognition of words in terms of the acti-vation of the constituent linguistic features (calledthe logogens). Each logogen has got a base ac-tivation value (also called the resting activation)that facilitates the recognition process. The restingactivation of a given logogen is determined by itsfrequency of occurrence. That is, high frequencywords have higher base activation value than thelow frequency words. The search model, on theother hand, assumes that words are organized ac-cording to their frequencies and are searched se-rially. (Taft and Hambly, 1986) have a proposedhybrid model that includes features of both theactivation and serial search process. The inter-active activation (IA) model (Diependaele et al.,2010) follows the connectionist approach and alsoincorporates the logogen model. In this frame-work, a word is initially perceived via the basicorthographic, features which in turn activate thehigher level syntactic and semantic features. TheIA model also accounts for the word superiorityeffect that assumes alphabets are recognized moreaccurately and quickly when they occur in a wordas compared to a non-word (Grainger and Jacobs,1996). An important extension of the IA modelis the dual-rout cascaded (DRC) model (Coltheartet al., 2001). This model assumes two paral-lel process of word recognition: the lexical routeand the sub-lexical route. The lexical route ac-counts for the recognition process through the par-allel activation of the orthographic and phonolog-ical features of a word. On the other hand, thesub-lexical route possesses a serial processor thatconverts graphemic representations into phonemicforms. As an alternative to two different process-ing paths n the DRC model, the parallel distributedprocessing model (PDP) (Seidenberg and McClel-land, 1989) has proposed a single architecture toexplain different processing outputs. The modelincorporate the distributed nature by assuming thateach word is associated with some distinct activa-tion pattern across a common set of features usedto recognize the word. The features may include,orthography, phonology, morphology or semantic.Generalizations of the PDP model for non-wordsand irregular words have been proposed by (Plautet al., 1996)3 ParticipantsIn order to understand how the different cognitiveprocesses vary across different user groups, twocategories of users have been considered for eachuser study. Group 1 consists of 25 native usersof Bangla in the age range 21-25 years, who arepursuing college level education and group 2 con-sists of 25 native users in the age range 13 to 17years (refer to figure 1). In this paper, the vari-ations in age and years of education have beentaken into account. Moreover, we have considereda distribution over medium to low socio-economicsections with monthly household income rangesINR 4500 to INR 15000.</s>
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<s>The Socio-EconomicClassification (SEC) has been performed accord-ing to the guidelines by the Market Research Soci-ety of India (MRSI) 2. MRSI has defined 12 socio-economic strata: A1 to E3 in the decreasing or-der. The containment of the socio-economic rangewas necessary as it directly affects education, lit-eracy and thus the state of comprehension skills ofa reader. In addition, to capture the first-languageskill, each native speaker was asked to rate his/herproficiency in Bangla on a 1-5 scale (1: very poorand 5: very strong), see figure 2.Figure 1: Participants’ details2http://imrbint.com/research/The-New-SEC-system-3rdMay2011.pdf428Figure 2: Proficiency in the mother tongue4 Data preparationFrom a Bangla corpus 3 of about 400,000 uniquewords, we have sampled 3500 words for the study.The words were selected in such a way that theyrepresent the ‘average’ words over the corpus. Themedian values of word frequency distribution andlength distribution lie at 368 and 5 respectively(refer to figur 3 for some sample words used inexperiment). In a psycholinguistics, to preservethe experimental standard, it is essential to restrictthe participants from making any strategic guessabout the input stimuli. This has been achieved byrandomly introducing non-words in between thevalid words during the experiment. However, de-signing non-words are a non-trivial process, andoften the reader’s response to the different typesof non-words opens up new insights into the pro-cess of word comprehension. Some examples ofnon-words are provided in figure 4.5 Experimental ProcedureWe have conducted lexical decision task (LDT)experiment (Meyer and Schvaneveldt, 1971) tostudy the visual recognition of Bangla words bynative speakers. In this experiment, a participantis presented with a visual input, generally, a stringof letters that can be words, non-words or pseudowords. Their task is to indicate, whether the pre-sented stimulus is a valid Bangla word or not. Thereaction time against each participant and the ac-curacy against each experimental stimulus acrossall the participants are recorded for further analy-sis. The time window for a user to submit any re-sponse has been set at 4 seconds, failing that a No3The Unicode corpus of Bangla was developed by the au-thors as a part of a broader study, the details are not in scopeof this paper.Response is recorded. In either cases it is followedby hash signs (####) followed by the next letterstring with 2.5 second delay. No response againsta stimulus is automatically recorded as wrong re-sponse by the user.Fifty users from the two target user groups par-ticipated in the LDT experiment. The 5000 exper-imental words (2500 words and 2500 non-words)were distributed randomly among 67 equal sized75-word sets. Each user was presented maximumof three sets a day with at least one hour gap be-tween two sets. Before recording experimentaldata, a sample set made up of 20 words was pre-sented to the users to make them accustomed withthe experiment.6 ObservationsAll the incorrect responses and extreme reactiontimes (RT: the time taken to respond to a stimuli)have been discarded. Participants and experimen-tal words having less than 70% accuracy have alsobeen discarded. Finally, 440 words with RT of 42(22 from group 1 and 20 from group 2) partici-pants have been used for further study.The RTs of each user have been normalized byz-transformation</s>
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<s>(Balota et al., 2007). The meanz-score over all users for a word has been com-puted. Negative z-scores indicate shorter responselatencies. Paired t-test has been performed be-tween results of the two user groups and p < 0.05has been found signifying the difference betweenreading characteristics of the two user groups.Next, we have studied the influence of differentword features on the outcome of the lexical deci-sion task. The word features studied in this paperhave been selected based on their prominence inthe literature (Yarkoni et al., 2008) and their rele-vance with respect to Bangla. The features are:•Morphological Family size: The morpholog-ical family size of a word w comprises of all theinflected, derived and compound paradigms thatcontains the word w (De Jong IV et al., 2000).•Word length (linear): The length is measuredin terms of the number of visual units or akshars;as Bangla belongs to the abugida group, mere al-phabetic word length does not reflect the difficultyencountered in reading (Sinha et al., 2012b).•Number of complex characters in a word:Complex characters are the consonant conjunctsor jukta-akshars present in a word.•Number of unique shapes in a word:429Figure 3: Examples of valid-words for experiment 4Figure 4: Construction of non-words for experimentBangla script uses the space in a non-linear wayand the akshars hangs from a distinct horizontalhead-stroke called mAtrA. The letters are made upof combinations of different shapes or strokes. Alltogether 57 unique strokes have been identifiedand indexed accordingly. The initial hypothesis isthat more the number of distinct shapes in a word;the more difficult it is to comprehend.•Orthographic word complexity: During vi-sual word recognition, the reader has to recog-nize the orthographic patterns (Selfridge, 1958).Word level representations interact with the letterlevel representations i.e, the characteristic shapesor strokes (refer to). As no standard dataset onFigure 5: characteristics strokes of Bangla aksharsshape combinations in Bangla letters is available,the unique shapes or strokes have been identifiedintuitively across all the Bangla letters includingthe consonant conjuncts. The Bangla Akademifont has been considered as standard Bangla or-thography. All together 57 unique strokes havebeen identified and numbered. Every Bangla letterhas been represented as a combination of the con-stituent shapes. To capture the interactive natureFigure 6: Mapping of Bangla akshars to character-istics shapesof visual complexity, an orthographic complexity430model has been derived in the following way:(a) The difficulty (d(s)) of a characteristic shape(i) or stroke is inversely proportional to its fa-miliarity or frequency (f(s)). The frequencyof the shapes has been calculated from theunique word list of the Bangla corpus with-out considering the frequency of each word.d(s) = 1/f(s). (1)(b) The difficulty (d(a)) of an akshar (a) dependson the sum of the complexity of its shapesnormalized by the number of shapes (n)d(a) = 1/nd(si) (2)(c) Finally, the difficulty (d(w)) of a word (w) isthe sum of the complexity of its constituentakshars normalized by word length (l) andmultiplied by the inverse of the word fre-quency (f(w))d(w) = 1/f(w)1/ld(aj)(3)•Orthographic & phonological neighborhood:We have constructed akshar based, orthographicshape based and phonological pattern basedneighborhood structure. The akshar baseddistance measure treats all akshars as of samevisual complexity regardless of their orthographicproperties, this is the reason distance</s>
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<s>amongwords based on orthographic strokes has beentreated separately. At each level of orthographicinformation, the neighbors have been categorizedinto three groups based on their distance from thegiven word.•Number of syllables: The syllabification ofthe Bangla words has been performed using aBangla Grapheme to Phoneme conversion tool,developed inhouse.•Semantic neighborhood: This measurerepresents the number of semantic neighborsof a word within the lexical organization of thelanguage. This is computed from the semanticlexicon described in (Sinha et al., 2012a).The mean and standard deviation values of theword features described above have been pre-sented in figure 7. We have analyzed the RT cor-responding to the above features using Spearman’scorrelation coefficient. The coefficient values be-tween each word attribute and word recognitionperformance for the two user groups have beenpresented in figure 8.From 8 we can observe that the correlationcoefficients values for lexical decision latenciesand decision accuracies are always less than 0.5,though they are different for different groups. Thedifference in the coefficient values may be at-tributed to the different reading patterns of the twogroups. Number of syllables has similar corre-lation coefficients as word length because mostoften the akshars boundaries match the phono-logical syllable boundaries. The measure of or-thographic word complexity possess low correla-tion coefficients with reaction times and accura-cies, this can be an outcome of considering onlythe orthographic attributes of a word, isolating itfrom the phonological or semantic dimensions. Infuture, the measure needs to be augmented withthose word features.Number of unique shapes and complex char-acters also do not show significant correlation.Spelling to sound consistency also has a moder-ate correlation with the groups. This shows thatspeakers are not much sensitive towards the minorinconsistencies in spelling to sound mapping. Thecorrelation coefficients of distant orthographic andphonological neighbors, immediate orthographicneighbors at shape level and semantic neighbor-hood are not significant for both groups. These in-dicate that after a threshold distance, the similar-ity or dissimilar-ity of the given word with otherwords in vocabulary does not affect the readersdecisions. In addition, at shape level, the num-ber of immediate orthographic neighbors may beunimportant due to the fact that often an akshar isconstituted with more than 2 characteristic ortho-graphic shapes and therefore, while reading, suchminor changes in orthographic properties may gounnoticed.Finally, the present calculation of semanticneighborhoods has been based on exhaustive lan-guage information (Sinha et al., 2012c), but theactual users may not possess such deep languageknowledge and therefore are less affected by thesemantic neighborhood structure. On the otherhand, the number of senses or meaning of a worddoes not have inhibitory effect on the decisionmaking process as the no ambiguity had to be re-solved here, instead the use of a word in different431Figure 7: Properties of valid words for experimentcontexts have increased its chance of encounteringwith the native readers of Bangla more often.Moreover, the decisions against non-words areequally interesting to the decisions against thevalid words. Non-words such as kakShataNa [cor-rect: (katakShaNa, time duration) ], AkampIta[correct: (akampita, steady)] and TAlAN [cor-rect: (cAlAna, transaction)] have almost alwaysbeen perceived as correct words by the readers dueto their orthographic and phonological proximityto the correct words. On the other hand, propernon-word i.e, an arbitrary letter string</s>
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<s>such as Na-jatathI has been accurately classified as invalid.This indicates that the cognitive processes of read-ing are sensitive to the probability of what aksharpattern can occur in a valid Bangla word.7 ConclusionIn this paper, we have presented a study on thecomprehension difficulty of visual word recog-nition in Bangla stored as a lexical decisiondatabase. Number of interesting observations hasbeen made from the experimental data and the ob-servations have been complemented with rationalinferences based on them. The correlation coef-ficients among word attributes and reaction timedata has revealed that individually no feature has alarge covariance factor, but the collective effect ofall of them determines the cognitive load for com-prehension. Moreover, using a reference languagecorpus based only on text from printed sources hasproven to be a short-coming for drawing meaning-ful inferences. Some initial insights on the deci-sions corresponding to the non-words have alsobeen presented.ReferencesDavid A Balota, Michael J Cortese, Susan D Sergent-Marshall, Daniel H Spieler, and MelvinJ Yap.2004. Visual word recognition of single-syllablewords. Journal of Experimental Psychology: Gen-eral, 133(2):283.D.A. Balota, M.J. Yap, K.A. Hutchison, M.J. Cortese,B. Kessler, B. Loftis, J.H. Neely, D.L. Nelson,G.B. Simpson, and R. Treiman. 2007. The en-glish lexicon project. Behavior Research Methods,39(3):445–459.M. Coltheart, B. Curtis, P. Atkins, and M. Haller. 1993.Models of reading aloud: Dual-route and parallel-distributed-processing approaches. PsychologicalReview; Psychological Review, 100(4):589.Max Coltheart, Kathleen Rastle, Conrad Perry, RobynLangdon, and Johannes Ziegler. 2001. Drc: a dual432Figure 8: Correlation analysis between word attributes and data from LDT (correlation coefficientsmarked with # are not significant (p-value > 0.05)433route cascaded model of visual word recognition andreading aloud. Psychological review, 108(1):204.Nivja H De Jong IV, Robert Schreuder, and R Har-ald Baayen. 2000. The morphological family sizeeffect and morphology. Language and cognitiveprocesses, 15(4-5):329–365.K. Diependaele, J.C. Ziegler, and J. Grainger. 2010.Fast phonology and the bimodal interactive activa-tion model. European Journal of Cognitive Psychol-ogy, 22(5):764–778.Kenneth I Forster and Elizabeth S Bednall. 1976. Ter-minating and exhaustive search in lexical access.Memory & Cognition, 4(1):53–61.Stephen J Frost, W Einar Mencl, Rebecca Sandak,Dina L Moore, Jay G Rueckl, Leonard Katz,Robert K Fulbright, and Kenneth R Pugh. 2005. Afunctional magnetic resonance imaging study of thetradeoff between semantics and phonology in read-ing aloud. NeuroReport, 16(6):621–624.Jonathan Grainger and Arthur M Jacobs. 1996. Or-thographic processing in visual word recognition:a multiple read-out model. Psychological review,103(3):518.David E Meyer and Roger W Schvaneveldt. 1971. Fa-cilitation in recognizing pairs of words: evidence ofa dependence between retrieval operations. Journalof experimental psychology, 90(2):227.John Morton. 1969. Interaction of information in wordrecognition. Psychological review, 76(2):165.Wayne S Murray and Kenneth I Forster. 2004. Serialmechanisms in lexical access: the rank hypothesis.Psychological Review, 111(3):721.David C Plaut, James L McClelland, Mark S Seiden-berg, and Karalyn Patterson. 1996. Understandingnormal and impaired word reading: computationalprinciples in quasi-regular domains. Psychologicalreview, 103(1):56.Mark S Seidenberg and James L McClelland. 1989. Adistributed, developmental model of word recogni-tion and naming. Psychological review, 96(4):523.Mark S Seidenberg. 2013. The science of reading andits educational implications. Language learning anddevelopment, 9(4):331–360.Oliver G Selfridge. 1958. Pandemonium: a paradigmfor learning in mechanisation of thought processes.M. Sinha, T. Dasgupta, and Basu A. 2012a. A complexnetwork analysis of syllables in bangla through syl-lablenet. In</s>
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<s>Sobha L Girish Nath Jha, Kalika Bali,editor, Workshop on Indian Language and Data:Resources and Evaluation, LREC, pages 131–138,May.M. Sinha, S. Sharma, T. Dasgupta, and Basu A. 2012b.New readability measures for bangla and hindi texts.Communicated in the 24th International Conferenceon Computational Linguistics,2012, IIT Bombay,August.Manjira Sinha, Abhik Jana, Tirthankar Dasgupta, andAnupam Basu. 2012c. A new semantic lexiconand similarity measure in bangla. In Proceedings ofthe 3rd Workshop on Cognitive Aspects of the Lexi-con, pages 171–182, Mumbai, India, December. TheCOLING 2012 Organizing Committee.Marcus Taft and Gail Hambly. 1986. Exploring the co-hort model of spoken word recognition. Cognition,22(3):259–282.T. Yarkoni, D. Balota, and M. Yap. 2008. Mov-ing beyond coltheart?s n: A new measure of ortho-graphic similarity. Psychonomic Bulletin & Review,15(5):971–979.Jason D Zevin and David A Balota. 2000. Prim-ing and attentional control of lexical and sublexi-cal pathways during naming. Journal of Experimen-tal Psychology: Learning, Memory, and Cognition,26(1):121.434</s>
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<s>FULL TITLE HERE IN ALL CAPS IN A FORMATA SYSTEM FOR CHECKING SPELLING, SEARCHING NAME & PROVIDING SUGGESTIONS IN BANGLA WORD MD HAIBUR RAHMAN Student Id: 012102013 A Thesis The Department Computer Science and Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Master of Science in Computer Science and Engineering United International University Dhaka, Bangladesh February, 2018 © MD HABIBUR RAHMAN, 2018 Approval Certificate This thesis titled " Avro for Bangla and its Application to Spelling checker, Transliteration and Name Searching” submitted by Md Habibur Rahman, Student ID: 012102013, has been accepted as Satisfactory in fulfillment of the requirement for the degree of Master of Science in Computer Science and Engineering on 27.02.2018. Board of Examiners ______________________________ Supervisor Prof. Dr. Mohammad Nurul Huda Professor & Coordinator - MSCSE United International University ______________________________ Head Examiner Novia Nurain Assistant Professor, CSE United International University ______________________________ Examiner-I Mohammad Moniruzzaman, Assistant Professor, CSE United International University ______________________________ Examiner-II Suman Ahmmed, Assistant Professor, CSE United International University ______________________________ Ex-Officio Swakkhar Shatabda, Associate Professor United International University Declaration This is to certify that the work entitled “Avro for Bangla and its Application to Spelling checker, Transliteration and Name Searching " is the outcome of the research carried out by me under the supervision of Prof. Dr. Mohammad Nurul Huda. ________________________________________ Md Habibur Rahman Studen ID: 012102013 Department: Computer Science and Engineering In my capacity as supervisor of the candidate’s project, I certify that the above statements are true to the best of my knowledge. ____________________________ Dr. Mohammad Nurul Huda Professor & Coordinator - MSCSE iii Abstract This thesis presents an improved phonetic encoding for Bangla which can be used for spelling checking, transliteration, name searching application as well as cross-lingual information retrieval. To produce an appropriate phonetic code for Bangla is always a significant challenge because of the complex and often inconsistent rules of Bangla words. We propose a phonetic encoding technique for Bangla considering the various Context-sensitive rules which includes the large repertoire of conjuncts in Bangla. Here we used Edit Distance Algorithm, Soundex Algorithm and Metaphone Algorithm for our proposed system. After implementation all of the said algorithms, we will get our targeted word within shortest possible time. ACKNOWLEDGMENTS At the very outset, I would like to express my deep gratitude to Almighty Allah who gave me enough knowledge and patience to complete the thesis within the stipulated time. I would like to give special thanks my supervisor Prof. Dr. Mohammad Nurul Huda for his precious as well as constructive suggestions during the planning and development of this research work. I would also like to extend my thanks to the concerned faculty member of United International University for their kind support throughout the research work. Finally, I wish to thank my parents for their encouragement through my study. Table of Contents LIST OF TABLES .......................................................................................................... vi LIST OF FIGURES ....................................................................................................... vii INTRODUCTION .............................. ……………….Error! Bookmark not defined. PHONETIC ENCODING .............................................................................................. 1 PROPOSED ENCODING ............................................................................................. 7 APPLICATIONS OF PHONETIC ENCODING ........................................................</s>
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<s>10 CONCLUSION ........................................................................................................... 34 References.................................................................................................................... 36 LIST OF TABLES Table 1: Soundex encoding table ......................................................................................... 3 Table 2: Phonetic Encoding Table ....................................................................................... 7 Table 3: Table for direct mapping ..................................................................................... 10 Table 4: Example of Edit distance ..................................................................................... 15 Table 5: Performance of Encoding .................................................................................... 16 Table 6: Distribution of Error ............................................................................................ 17 Table 7: Proposed Name searching for Bangla using direct mapping............................... 17 vii LIST OF FIGURES Figure 1: The Soundex algorithm ........................................................................................ 4 Figure 2: Proposed Techniques ......................................................................................... 27 Figure 3: sample output for ফণ .......................................................................................... 28 Figure 4: Sample output ড ই য ....................................................................................... 28 Figure 5: Sample output for ত ভ যয ................................................................................. 29 Chapter I: INTRODUCTION Bangla is one of the widely spoken languages, especially in the Indian Subcontinent. The Bengali spelling rules are very complex in nature. One of the basic reasons for this is its consonant clusters or juktakkhors. Some other notable reasons for its complexity are phonetic similarity of the characters, the difference between the rapheme representation and the phonetic utterances etc. Phonetic encoding for Bangla is always a great challenge for its complex nature of letters or words. The first encoding for Bangla was based on Soundex method which was not able to handle the complexity of Bangla spelling rules. In this particular thesis paper, we will describe phonetic encoding elaborately in Chapter II. Then, we will discuss the scope and importance of our encoding as well as the limitations of other encoding in Chapter III. After that, we will propose our encoding with reasoning in Chapter IV. Next, we will explain the methodology of our new application for Bangla in details in Chapter V. Finally, we will summarize how our new system would perform better than the existing systems. CHAPTER II: PHONETIC ENCODING 2.1. Definition Based on the pronunciation of string, code is done. The input of a phonetic encoding algorithm is a word and the result is an encoded key that should be same for all words which are pronounced similarly that allows for a reasonable amount of fuzziness. As for instance, metaphone encoding gives the code RLS for the word analyze in English. It is known that analyze and analise have the same pronunciation. Hence, a good encoding in English should be able to give the same code RLS to analyze as well. 2.2 Phonetic Encoding for English Various types of approximate string matching algorithms are Soundex, Metaphone, Double metaphone and PHONIX in English. These phonetic matching algorithms make partition the consonants by phonetic similarity then use a single key to encode each set. Only the first few consonant sounds are encoded unless the first letter is a vowel for the said algorithms. 2.3 Soundex Soundex partitions the set of letters into seven disjoint sets assuming that the letters in the same set have similar sound. Each of these sets is given a unique key except for the set containing the vowels and the letters h, w, and y which are considered to be silent and is not</s>
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<s>considered during encoding. The Soundex codes are shown in Table 1: Soundex encoding table. The Soundex algorithm transforms all but the first letter of each string into the code, then truncates the result to be at most four characters long. Zeros are added at the end if necessary to produce a four-character code. For example,Washington is coded W-252 (W, 2 for the S, 5 for the N, 2 for the G, remaining letters disregarded), and Lee is coded L-000 (L, 000 added). Soundex deals with small table size and works based on letter-by-letter algorithm. As a result, it is more speedy than other phonetic methods. Table 1: Soundex encoding table Code Letters 0(not coded) A, E, I, O, U, H, W, Y 1 B, F, P, V 2 C, G, J, K, Q, S, X, Z 3 D, T 4 L 5 M, N 6 R 1. Capitalize all letters in the word and drop all punctuation marks. Pad the word with Right most blanks as needed during each procedure step. 2. Retain the first letter of the word. 3. Change all occurrence of the following letters to '0' (zero): 'A', E', 'I', 'O', 'U', 'H', 'W', 'Y'. 4. Change letters from the following sets into the digit given: • 1 = 'B', 'F', 'P', 'V' • 2 = 'C', 'G', 'J', 'K', 'Q', 'S', 'X', 'Z' • 3 = 'D','T' • 4 = 'L' • 5 = 'M','N' • 6 = 'R' 5. Remove all pairs of digits which occur beside each other from the string that resulted after step (4). 6. Remove all zeros from the string that results from step 5.0 (placed there in step 3) 7. Pad the string that resulted from step (6) with trailing zeros and return only the first Four positions, which will be of the form <uppercase letter> <digit> <digit> <digit>. Figure 1: The Soundex algorithm 2.4 Metaphone The Metaphone algorithm analyzes both single consonants and groups of letters called diphthongs based on a set of rules for grouping consonants, then mapping groups to Metaphone codes. The Metaphone Rules Metaphone reduces the alphabet to 16 consonant sounds: B X S K J T F H L M N P R 0 W Y That isn't an O but a zero - representing the 'th' sound. Transformations Metaphone uses the following transformation rules: Doubled letters except "c" -> drop 2nd letter. Vowels are only kept when they are the first letter. B -> B unless at the end of a word after "m" as in “dumb" C -> X (sh) if -cia- or -ch- S if -ci-, -ce- or -cy- K otherwise, including -sch- D -> J if in -dge-, -dgy- or -dgi- T otherwise F -> F G -> silent if in -gh- and not at end or before a vowel in -gn- or -gned- (also see dge etc. above) J if before i or e or y if not double gg K otherwise H -> silent if after vowel and no</s>
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<s>vowel follows H otherwise J -> J K -> silent if after "c" K otherwise L -> L M -> M N -> N P -> F if before "h" P otherwise Q -> K R -> R S -> X (sh) if before "h" or in -sio- or -sia- S otherwise T -> X (sh) if -tia- or -tio- 0 (th) if before "h" silent if in -tch- T otherwise V -> F W -> silent if not followed by a vowel W if followed by a vowel X -> KS Y -> silent if not followed by a vowel Y if followed by a vowel Z -> S Initial Letter Exceptions Initial kn-, gn- pn, ac- or wr- -> drop first letter Initial x- -> change to "s" Initial wh- -> change to "w" CHAPTER III: PROPOSED ENCODING We needed to keep few things in our mind while proposing this encoding. We particularly considered the phonetic similarity of letters to give them the same code and also to keep in mind the orthographic or spelling rules as well as to know how letters spell in different context so that we can encode the letters with similar sounding letters considering the context.Using this encoding, anyone would be able to work as an intermediate code in multi-lingual applications. We will be encoding our Bangla letters to a set of Latin alphabets so that it can easily work as an intermediate language to work with English. We assume that the Bangla text is encoded using Unicode Normalization Form C (NFC). 3.1 Proposed phonetic encoding for words We will have two encoding- mainly one for words and a few variations from it for names as well. This section describes about the words encoding. Throughout the thesis paper, we termed our proposed phonetic encoding by Avro phonetic encoding or proposed phonetic encoding. In order to encode Bangla words, we need to consider context and also need to generate multiple codes for the same string. These constraints can be handled in Edit Distance, Soundex and metaphone algorithm, which we did for Bangla here. That’s why, we termed it as metaphone phonetic encoding. 3.2 Phonetic Encoding Following Table 2: Phonetic Encoding table for words is the table of proposed Avro phonetic encoding for words. Followed by the table, there will be reasoning of each of the encoding. Table 2: Phonetic Encoding Table Letter Name ASCII Code O অ 2437 A আ 2438 I ই 2439 I ঈ 2440 U উ 2441 U ঊ 2442 rri ঋ 2443 E এ 2447 OI ঐ 2448 O 2451 OU 2452 K ও 2453 kh ঔ 2454 G ক 2455 ghgh খ 2456 ng গ 2457 C ঘ 2458 ch ঙ 2459 J চ 2460 jh ছ 2461 NG ঞ 2462 T ট 2463 Th ঠ 2464 D ড 2465 Dh ঢ 2466 N ণ 2467 T ত 2468 th থ 2469 D দ 2470 dh ধ 2471 N ন 2472 P 2474 ph প 2475</s>
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<s>B ফ 2476 bh ব 2477 m ভ 2478 Z ম 2479 R য 2480 L র 2482 sh 2486 S ল 2487 s 2489 a া 2494 i িা 2495 I া 2496 u া 2497 U া 2498 e ো 2503 OI ৈা 2504 O 2507 OU ো 2508 hs া 2509 TH ৎ 2510 R ড় 2524 Rh ঢ় 2525 Y 2527 o া 2433 ng া 2434 3.3 Existing Phonetic Encoding for Bangla Eighty years old technique of phonetic encoding is new in Bangla which was first proposed by Hoque and Kaykobad in 2002. Then Zaman and Khan, 2004, proposed their version of soundex type Bangla phonetic encoding. Both of the encoding use “soundex” in their encoding name. The cause behind it is they follow the general principal of soundex encoding, to partition the letters in to disjoint sets. CHAPTER IV: APPLICATIONS OF PHONETIC ENCODING Without being properly used in applications, phonetic encoding would not be able to play significant role for a language. Name searching was first such application in which phonetic encoding was used after that spelling checker adopts this phonetic encoding technique. We have used our phonetic encoding in many applications like spelling checker, transliteration, cross-lingual information retrieval and name searching for Bangla. In every case, we will first show how that application were developed earlier, how they perform and then how phonetic encoding improves its performance. 4.1 Translation using Direct Mapping Some software exactly uses this mapping. We are giving a mapping, which we used for our direct mapping transliteration. Since this direct mapping is still a phonetic mapping, the difference is, it will not look up in the dictionary if it has any word with same pronunciation. We have introduced an intermediate encoding which will be used to encode before converting. We need it because in some cases it should not be converted directly, like bool pronounce as bul, hence before mapping we convert “oo” to “u”. One more thing is we will not only consider one letter for one to one mapping, we may sometime consider bigrams for mapping. Because, to represent some Bangla letters phonetically in English we use those bigrams. Like for Bangla letter খ/kh/ we use kh. Table 3: Table for direct mapping case (char)2433: engText.Append("o"); break; //chandra-bindu (char)2434: engText.Append("ng"); break; //onesh-kar case (char)2435: /*engText.Append(":");*/ break; //khandata case (char)2437: engText.Append("o"); break; //'অ' case (char)2438: engText.Append("a"); break; //'আ' case (char)2439: engText.Append("e"); break; //'ই' case (char)2440: engText.Append("E"); break; //'ঈ' case (char)2441: engText.Append("u"); break; //'উ' case (char)2442: engText.Append("U"); break; //'ঊ' case (char)2443: engText.Append("rri"); break; //'ঋ' case (char)2447: engText.Append("e"); break; //'এ' case (char)2448: engText.Append("OI"); break; //'ঐ' case (char)2451: engText.Append("O"); break; //'' case (char)2452: engText.Append("OU"); break; //'' case (char)2453: engText.Append("k");/*engText.Append("ko");*/break; //'ও' case (char)2454: engText.Append("kh");/*engText.Append("kha");*/break; //'ঔ' case char)2455: engText.Append("g");/*engText.Append("go");engText.Append("G");*/break; //'ক' case (char)2456: engText.Append("GH"); break; //'খ' case (char)2457: engText.Append("N");/*engText.Append("g");*/break; //'গ' case (char)2458: engText.Append("c");/*engText.Append("co");*/break; //'ঘ' case (char)2459: engText.Append("ch");/*engText.Append("CH");*/break; //'ঙ' case (char)2460: engText.Append("j");/*engText.Append("jo");*/break; //'চ' case (char)2461: engText.Append("jh"); break; //'ছ' case (char)2462: /*engText.Append("n");*/ break; //nio case (char)2463: engText.Append("T");/*engText.Append("To");*/break; //'ট' case (char)2464: engText.Append("Th");/*engText.Append("TH");*/break; //'ঠ' case</s>
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<s>(char)2465: engText.Append("D");/*engText.Append("Do");*/break; //'ড' case (char)2466: engText.Append("Dh");/*engText.Append("DH");*/break; //'ঢ' case (char)2467: engText.Append("N");/*engText.Append("No");*/break; //'ণ' case (char)2468: engText.Append("t");/*engText.Append("to");*/break; //'ত' case (char)2469: engText.Append("th");/*engText.Append("tho");*/break; //'থ' case (char)2470: engText.Append("d");/*engText.Append("do");*/break; //'দ' case (char)2471: engText.Append("dh"); break; //'ধ' case (char)2472: engText.Append("n");/*engText.Append("no");*/break; //'ন' case (char)2474: engText.Append("p");/*engText.Append("po");*/break; //'' case (char)2475: engText.Append("ph");/*engText.Append("f");*/break; //'প' case (char)2476: engText.Append("b");/*engText.Append("bo");*/break; //'ফ' case (char)2477: engText.Append("bh");/*engText.Append("BH");engText.Append("v");*/break; //'ব' case (char)2478: engText.Append("m");/*engText.Append("mo");*/break; //'ভ' case (char)2479: engText.Append("z"); break; //'ম' case (char)2480: engText.Append("r");/*engText.Append("ro");*/break; //'য' case (char)2482: engText.Append("L");/*engText.Append("Lo");*/break; //'র' case (char)2486: engText.Append("sh");/*engText.Append("S");*/break; //'' case (char)2487: engText.Append("S");/*engText.Append("h");*/break; //'ল' case (char)2488: engText.Append("s");/*engText.Append("so");*/break; //'' case (char)2489: engText.Append("h");/*engText.Append("ho");*/break; //'' case (char)2494: engText.Append("a"); break; // a-kar case (char)2495: engText.Append("i"); break; // rossi-kar case (char)2496: engText.Append("I"); break; // dirghi-kar case (char)2497: engText.Append("u"); break; // rossu-kar case (char)2498: engText.Append("U"); break; // dighu-kar case (char)2503: engText.Append("e"); break; //a-kar case (char)2504: engText.Append("OI"); break; //oi-kar case (char)2507: engText.Append("O"); break; //o-kar case (char)2508: engText.Append(","); break; //oaau-kar case (char)2509: /*engText.Append("OU");*/break; //hosonta case (char)2510: engText.Append("t"); break; //khandata case (char)2524: engText.Append("R");/*engText.Append("Ro");*/break; //'ড়' case (char)2525: engText.Append("Rh"); break; //'ঢ়' case (char)2527: engText.Append("Y");/*engText.Append("Yo");*/break; //'' case ' ': engText.Append("kkh");break; 4.2 Phonetic mapping In phonetic mapping, the basic idea is to check in the dictionary if we have the word with same pronunciation. Following is the algorithm of phonetic mapping. Algorithm of phonetic mapping if there is a word with the same pronunciation in the dictionary then convert it to that word else if there are multiple words with the same pronunciation in the dictionary then give suggestions for that word and the user will select which one to use else if there are not words with the same pronunciation in the dictionary then convert it using direct mapping Now our main challenge is how we can get the pronunciation of a Bangla word to check it with an English word and understand it has the same pronunciation. We have used the phonetic encoding for Bangla proposed in section 4.1. That encoding encodes Bangla word in to an English word that represents the pronunciation of a word. So, our only challenge is to convert the English words in the same manner so that both encoding are consistent. For example, is encoded in to klm. 4.3 Spelling Checker Spelling Checker may be used for various applications like Optical Character Recognition (OCR), Machine Translation (MT), Natural Language Processing (NLP and so on. 4.4 Spelling error patterns There are two types of word-error such as non-word error and real-word error. Again, there are two types of errors which are typographical error phonetic error. Typographical errors may occur because of typing mistakes, negligence, lack of concentrations and may be for any other reasons. Phonetic errors may be happened because of not knowing the spelling of a desired word although the user knows the pronunciation of the word. In non-word errors, there are mainly two types of errors. One is typographical error and another is phonetic error. Description of typographical error is as follows. In an early study, found that 80% of all misspelled words (non-words errors) in a sample of human keypunched text were caused by single error misspellings: a single one of the following errors: </s>
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<s>Substitution error: mistyping the as ther Deletion error: mistyping the as th Insertion error: mistyping the as thw Transposition error: mistyping the as hte These are the type of typographical errors, which occurred due to typing mistakes, negligence, and lack of concentrations or other reasons. But if computer gives a red underline into the word, then we can easily correct it without seeing the spelling suggestions. But scenarios of phonetic errors are different. Phonetic errors occur when the user do not know the spelling of a desired word but knows the pronunciation of the word. So, using the pronunciation the user may write a word but in suggestion it is impossible to get the desired word in case of Bangla, because of complex Bangla rules. 4.5 Approximate string matching algorithm In our Thesis we use Levenshtein Edit Distance method for approximate string-matching algorithm. The algorithm use to check the closeness of dictionary words with the misspelled word. It gives suggestion that is closed to misspelled word. Levenshtein Edit Distance: Definition: The edit distance algorithm is similarity of two strings, s1 and s2, is defined as the minimum number of point mutations required to change s1 into s2, where a point mutation is one of Insert Letter Delete Letter Replace Letter Transpose Letter Levenshtein Edit Distance algorithm used various reporting purpose like Spell checking, Speech recognition, DNA analysis and Plagiarism detection. Example: e (“kitten”, “sitting”) = 3 Kitten sitten (substitution of “k” with “s”), Sitten sittin (substitution of “e” with “I”), Sittin sitting (insert “g” at the end), For example, we assume our lexicon consist of following words. Our misspelled word is কল. Now when we check the lexicon dictionary we find that there are no such word কল. So, it is a misspelled word according to this dictionary. Now to generate and rank the suggestion, we will generate the edit-distance with all the words of the dictionary. 4.6 How to Rank Performance of Encoding To rank the suggestion, we used both phonetic edit distance, which is edit distance between phonetic codes, and normal edit distance. We did not use the average of both, but preferred for a weighted average. For example, our score = a * phonetic_edit_distance + (1-a) * normal_edit_distance where, a > (1-a). We rank the suggestions according to the scored achieved for a word. Table 4: Example of Edit distance Hence, our ranked suggestion for ওর will be ওর , ও ও, ওথ , ভ র 4.7 Performance of our proposed encoding In our Lexicon Dictionary we have 110750 words for suggestions. Our proposed Encoding performance shows the performance when it used on 1607 commonly misspelled words. Firstly, we apply our encoding to both the correct and misspelled words, after complete the encoding of both word we use Edit Distance Algorithm for minimum distance measure. After implement the Edit Distance algorithm we have found few words which is lowest minimum distance (like=1). The words which is found from Edit Distance</s>
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<s>Algorithm at this stage will be implemented this words in Soundex Algorithm. The number of words will be reduced after the implementation of Soundex Algorithm. That means, the words which have less Edit Distance will be brought using Soundex Algorithm. Later on, the words found using Soundex Algorithm will be used in Metaphone Algorithm. After implementation of Metaphone Algorithm, we will get our targeted word it is considered correct if the edit distance is 0. In our case 130 out of 1607 words do not produce an edit distance of 0 with the correct word, which are termed as error, resulting in an accuracy of 91.91%. Table 5: Performance of Encoding Dictionary Word Edit Distance with Word No of Word 1607 Correct (Edit Distance 0) 1477 Error 130 Rate of Accuracy 91.91% Rate of Error 8.08% The numbers of unmatched words fall to 107 and 23 if we consider edit distances of 1 and 2 respectively, as shown in Table 6. Table 6: distribution of Error Error 130 Edit Distance =1 107 Edit Distance =2 23 After complete of our proposed technique we have got some suggestion list of words. It show words suggestion which have Edit Distance >=2. So, we can always get our expected words in suggestion list and more than 91.91% time’s word at the top of the suggestion list. 4.8 Example of transliteration ami bhal achi. Tomar khbor ki? Ajke shndha bela tumi ki Kroch. obak bepar hl, ami ekhon bangla likhte pari English diye. ar mjar bepar hl ami dui vhabe likhte pari. ek`ta daireckT arekta phnetik. Tmar desh e koto taka te Dlar. Ami abar jukt brn likhte pari. Output in direct mapping will be following. আিম ভ লল আিি। েত ম র খবর িক। আজলক সন্ধ্য েবল ত িম িক করি। অব ক বযপ র হল , আিম এখন ব ল িলখলত প ির। অভ্র িিয় । আর মজ র েবপ র হল আিম ি ই ভ লব িলখলত প ির। একট ড ইলরক্ট আলরকট ফলনটিক। েত ম র েিশ এ কত ট ক েত ডল র। আিম এই ভ লব আব র জক্ত বনন িলখলত প ির। Bangle Text: আিম ভ লল আিি। েত ম র খবর িক। আজলক সন্ধ্য েবল ত িম িক করি। অব ক বযপ র হল , আিম এখন ব ল িলখলত প ির। অভ্র িিয় । আর মজ র েবপ র হল আিম ি ই ভ লব িলখলত প ির। একট ড ইলরক্ট আলরকট ফলনটিক। েত ম র েিশ এ কত ট ক েত ডল র। আিম এই ভ লব আব র জ ক্ত বনন িলখলত প ির। Table 7: Proposed Name searching for Bangla using direct mapping private void ShowOutput(List<string> matches, string code, bool isShowMessageBox , System.Windows.Forms.RichTextBox rtb) if(isShowMessageBox) StringBuilder builder = new StringBuilder(); builder.Append("Searching for:\r\n"); builder.AppendFormat("{0} ({1})\r\n\r\n", txtFind.Text, code); if (matches.Count > 0) builder.AppendFormat("Matches found ({0}):\r\n", matches.Count); foreach (string match in matches) builder.AppendFormat("{0}\r\n", match); else builder.Append("No matches found"); MessageBox.Show(builder.ToString()); else StringBuilder builder = new StringBuilder(); builder.Append("Searching for:\r\n"); builder.AppendFormat("{0} ({1})\r\n\r\n", txtFind.Text, code); if (matches.Count > 0)</s>
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<s>builder.AppendFormat("Matches found ({0}):\r\n", matches.Count); foreach (string match in matches) builder.AppendFormat("{0}\r\n", match); else builder.Append("No matches found"); rtb.Text = builder.ToString(); #endregion The transformation or rules described in Table 6: Proposed Name searching for Bangla that derived names from the Dictionary using direct mapping, if the inputted word is exists it show the word is match. If the inputted word not exist in Dictionary then it show the word not found in Dictionary. 4.9 Code for Name searching using Dictionary public partial class FormMeasurement : Form private static SpellCheck _Dictionary; public FormMeasurement() InitializeComponent(); #region Events private void buttonBrowse_Click(object sender, EventArgs e) textBoxDictionaryPath.ReadOnly = false; openFileDialog.InitialDirectory = System.IO.Path.GetFullPath(@"..\..\Dictionary"); openFileDialog.Title = "Browse Text Files"; openFileDialog.CheckFileExists = true; openFileDialog.CheckPathExists = true; openFileDialog.DefaultExt = "txt"; openFileDialog.Filter = "Text files (*.txt)|*.txt|All files (*.*)|*.*"; openFileDialog.FilterIndex = 2; openFileDialog.RestoreDirectory = true; openFileDialog.ReadOnlyChecked = true; openFileDialog.ShowReadOnly = true; if (openFileDialog.ShowDialog() == DialogResult.OK) textBoxDictionaryPath.Text = openFileDialog.FileName; textBoxDictionaryPath.ReadOnly = true; private void btnSearch_Click(object sender, EventArgs e) listViewSuggestionList.Items.Clear(); _Dictionary = new SpellCheck(File.ReadAllText(textBoxDictionaryPath.Text), textBoxDictionaryPath.Text.Contains("BD") ? true : false); string source = txtFind.Text; #region Edit Distance List<string> suggestions = _Dictionary.Correct(source); ListViewItem item; foreach (string targetString in suggestions) int distance = EditDistance.Compare(source.ToLower(), targetString.ToLower()); item = new ListViewItem(targetString); item.SubItems.Add(distance.ToString()); listViewSuggestionList.Items.Add(item); #endregion #region Soundex string[] names = suggestions.ToArray(); // List to hold matches List<string> matches = new List<string>(); string code = SearchSoundex(txtFind.Text, names, matches); ShowOutput(matches, code, false, richTextBoxSoundex); #endregion #region Metaphone // List to hold matches matches = new List<string>(); code = SearchMetaphone(txtFind.Text, names, matches); ShowOutput(matches, code, false, richTextBoxMetaphone); #endregion ShowOutput(matches, code, true, null); #endregion #region Soundex private string SearchSoundex(string find, string[] names, List<string> matches) find = ConvertSoundex(find); // Encode string we want to find string code = Soundex.Encode(find); // Search through the list of names foreach (string name in names) string soundex_name = ConvertSoundex(name); // Compare against soundex-encoded version of name if (Soundex.Encode(soundex_name) == code) // Found a match--add it to list //matches.Add(soundex_name); matches.Add(name); return code; private string ConvertSoundex(string text) StringBuilder engText = new StringBuilder(); for (int index = 0; index < text.Length; index++) switch (text[index]) case (char)2433: engText.Append("o"); break; //chandra-bindu case (char)2434: engText.Append("ng"); break; //onesh-kar case (char)2435: /*engText.Append(":");*/ break; //khandata case (char)2437: engText.Append("o"); break; //'অ' case (char)2438: engText.Append("a"); break; //'আ' case (char)2439: engText.Append("e"); break; //'ই' case (char)2440: engText.Append("E"); break; //'ঈ' case (char)2441: engText.Append("u"); break; //'উ' case (char)2442: engText.Append("U"); break; //'ঊ' case (char)2443: engText.Append("rri"); break; //'ঋ' case (char)2447: engText.Append("e"); break; //'এ' case (char)2448: engText.Append("OI"); break; //'ঐ' case (char)2451: engText.Append("O"); break; //'' case (char)2452: engText.Append("OU"); break; //'' case (char)2453: engText.Append("k");/*engText.Append("ko");*/break; //'ও' case (char)2454: engText.Append("kh");/*engText.Append("kha");*/break; //'ঔ' case (char)2455: engText.Append("g");/*engText.Append("go");engText.Append("G"); */break; //'ক' case (char)2456: engText.Append("GH"); break; //'খ' case (char)2457: engText.Append("N");/*engText.Append("g");*/break; //'গ' case (char)2458: engText.Append("c");/*engText.Append("co");*/break; //'ঘ' case (char)2459: engText.Append("ch");/*engText.Append("CH");*/break; //'ঙ' case (char)2460: engText.Append("j");/*engText.Append("jo");*/break; //'চ' case (char)2461: engText.Append("jh"); break; //'ছ' case (char)2462: /*engText.Append("n");*/ break; //nio case (char)2463: engText.Append("T");/*engText.Append("To");*/break; //'ট' case (char)2464: engText.Append("Th");/*engText.Append("TH");*/break; //'ঠ' case (char)2465: engText.Append("D");/*engText.Append("Do");*/break; //'ড' case (char)2466: engText.Append("Dh");/*engText.Append("DH");*/break; //'ঢ' case (char)2467: engText.Append("N");/*engText.Append("No");*/break; //'ণ' case (char)2468: engText.Append("t");/*engText.Append("to");*/break; //'ত' case (char)2469: engText.Append("th");/*engText.Append("tho");*/break; //'থ' case (char)2470: engText.Append("d");/*engText.Append("do");*/break; //'দ' case (char)2471: engText.Append("dh"); break; //'ধ' case (char)2472: engText.Append("n");/*engText.Append("no");*/break; //'ন' case (char)2474: engText.Append("p");/*engText.Append("po");*/break; //'' case (char)2475: engText.Append("ph");/*engText.Append("f");*/break; //'প' case (char)2476: engText.Append("b");/*engText.Append("bo");*/break; //'ফ' case (char)2477: engText.Append("bh");/*engText.Append("BH");engText.Append("v");*/break; //'ব' case</s>
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<s>(char)2478: engText.Append("m");/*engText.Append("mo");*/break; //'ভ' case (char)2479: engText.Append("z"); break; //'ম' case (char)2480: engText.Append("r");/*engText.Append("ro");*/break; //'য' case (char)2482: engText.Append("L");/*engText.Append("Lo");*/break; //'র' case (char)2486: engText.Append("sh");/*engText.Append("S");*/break; //'' case (char)2487: engText.Append("S");/*engText.Append("h");*/break; //'ল' case (char)2488: engText.Append("s");/*engText.Append("so");*/break; //'' case (char)2489: engText.Append("h");/*engText.Append("ho");*/break; //'' case (char)2494: engText.Append("a"); break; // a-kar case (char)2495: engText.Append("i"); break; // rossi-kar case (char)2496: engText.Append("I"); break; // dirghi-kar case (char)2497: engText.Append("u"); break; // rossu-kar case (char)2498: engText.Append("U"); break; // dighu-kar case (char)2503: engText.Append("e"); break; //a-kar case (char)2504: engText.Append("OI"); break; //oi-kar case (char)2507: engText.Append("O"); break; //o-kar case (char)2508: engText.Append(","); break; //oaau-kar case (char)2509: /*engText.Append("OU");*/break; //hosonta case (char)2510: engText.Append("t"); break; //khandata case (char)2524: engText.Append("R");/*engText.Append("Ro");*/break; //'ড়' case (char)2525: engText.Append("Rh"); break; //'ঢ়' case (char)2527: engText.Append("Y");/*engText.Append("Yo");*/break; //'' engText.Append("kkh");break; return engText.ToString(); #endregion #region Metaphone private string SearchMetaphone(string find, string[] names, List<string> matches) find = ConvertMetaphone(find); // Encode string we want to find Metaphone metaphone = new Metaphone(); string code = metaphone.Encode(find); // Search through the list of names foreach (string name in names) string metaphone_name = ConvertMetaphone(name); // Compare against soundex-encoded version of name if (metaphone.Encode(metaphone_name) == code) // Found a match--add it to list //matches.Add(metaphone_name); matches.Add(name); return code; private string ConvertMetaphone(string text) StringBuilder engText = new StringBuilder(); for (int index = 0; index < text.Length; index++) switch (text[index]) case (char)2437: engText.Append("o"); break; //'অ' case (char)2451: engText.Append("o"); break; //'' case (char)2438: engText.Append("a"); break; //'আ' case (char)2494: engText.Append("a"); break; // a-kar case (char)2439: engText.Append("i"); break; //'ই' case (char)2440: engText.Append("i"); break; //'ঈ' case (char)2495: engText.Append("i"); break; // rossi-kar case (char)2496: engText.Append("i"); break; // dirghi-kar case (char)2441: engText.Append("u"); break; //'উ' case (char)2442: engText.Append("u"); break; //'ঊ' case (char)2497: engText.Append("u"); break; // rossu-kar case (char)2498: engText.Append("u"); break; // dighu-kar case (char)2447: engText.Append("e"); break; //'এ' case (char)2503: engText.Append("e"); break; //a-kar case (char)2448: engText.Append("oi"); break; //'ঐ' case (char)2504: engText.Append("oi"); break; //oi-kar case (char)2452: engText.Append("ou"); break; //'' case (char)2508: engText.Append("ou"); break; //oaau-kar case (char)2453: engText.Append("k"); break; //'ও' case (char)2454: engText.Append("k"); break; //'ঔ' //case ' ': engText.Append("k"); break; case (char)2455: engText.Append("g"); break; //'ক' case (char)2456: engText.Append("g"); break; //'খ' case (char)2457: engText.Append("ng"); break; //'গ' case (char)2434: engText.Append("ng"); break; //onesh-kar case (char)2458: engText.Append("c"); break; //'ঘ' case (char)2459: engText.Append("c"); break; //'ঙ' case (char)2460: engText.Append("j"); break; //'চ' case (char)2461: engText.Append("j"); break; //'ছ' case (char)2479: engText.Append("j"); break; //'ম' case (char)4444: engText.Append("e"); break; //'ম'-phola case (char)2462: engText.Append("n"); break; //nio case (char)2463: engText.Append("T"); break; //'ট' case (char)2464: engText.Append("T"); break; //'ঠ' case (char)2465: engText.Append("D"); break; //'ড' case (char)2466: engText.Append("D"); break; //'ঢ' case (char)2443: engText.Append("ri"); break; //'ঋ' case (char)2480: engText.Append("r"); break; //'য' case (char)2524: engText.Append("r"); break; //'ড়' case (char)2525: engText.Append("r"); break; //'ঢ়' case (char)2472: engText.Append("n"); break; //'ন' case (char)2467: engText.Append("n"); break; //'ণ' case (char)2468: engText.Append("t"); break; //'ত' case (char)2469: engText.Append("t"); break; //'থ' case (char)2470: engText.Append("d"); break; //'দ' case (char)2471: engText.Append("d"); break; //'ধ' case (char)2474: engText.Append("p"); break; //'' case (char)2475: engText.Append("p"); break; //'প' case (char)2476: engText.Append("b"); break; //'ফ' case (char)2477: engText.Append("b"); break; //'ব' case (char)2478: engText.Append("m"); break; //'ভ' case (char)2527: engText.Append("y"); break; //'' case (char)2482: engText.Append("l"); break; //'র' case (char)2486: engText.Append("s"); break; //'' case (char)2487: engText.Append("s"); break; //'ল' case (char)2488: engText.Append("s"); break; //'' case (char)2489: engText.Append("h"); break; //'' case (char)58: engText.Append("h"); break; //bisarga case (char)2433: engText.Append("o"); break; //chandra-bindu //case (char)2507: engText.Append("O"); break; //o-kar</s>
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<s>//case (char)2509: /*engText.Append("OU");*/break; //hosonta return engText.ToString(); #endregion #region Private Method private void ShowOutput(List<string> matches, string code, bool isShowMessageBox , System.Windows.Forms.RichTextBox rtb) if(isShowMessageBox) StringBuilder builder = new StringBuilder(); builder.Append("Searching for:\r\n"); builder.AppendFormat("{0} ({1})\r\n\r\n", txtFind.Text, code); if (matches.Count > 0) builder.AppendFormat("Matches found ({0}):\r\n", matches.Count); foreach (string match in matches) builder.AppendFormat("{0}\r\n", match); else builder.Append("No matches found"); MessageBox.Show(builder.ToString()); else StringBuilder builder = new StringBuilder(); builder.Append("Searching for:\r\n"); builder.AppendFormat("{0} ({1})\r\n\r\n", txtFind.Text, code); if (matches.Count > 0) builder.AppendFormat("Matches found ({0}):\r\n", matches.Count); foreach (string match in matches) builder.AppendFormat("{0}\r\n", match); else builder.Append("No matches found"); rtb.Text = builder.ToString(); #endregion 4.10 Proposed Technique: Fig 2: Proposed Techniques In the figure 2: The sound that we have got after encoding the inputted sound is added in the dictionary for searching. If the given sound is found in the dictionary, then it will be displayed as correct word. Otherwise, at first using Edit Distance Algorithm of our proposed system, the least distance words will be brought from the dictionary. Here, only those words will be brought whose Distance Value = 1. The words found using Edit Distance Algorithm at this stage will be implemented using Soundex Algorithm in future. The number of words will be reduced after the implementation of Soundex Algorithm. That means, the words which have less Edit Distance will be brought using Soundex Algorithm. Later on, the words found using Soundex Algorithm will be used in Metaphone Algorithm. After implementation of Metaphone Algorithm, we will get our targeted word. If the word will not be found, it will be shown as wrong word using error message. At last, the desired word will be found using our proposed technique. Otherwise, the words close to the desired word will be displayed / showed as suggestion. In this way, the user will get his desired word. If after using this system the desired word will not be found, it will be shown as wrong word to the user. Sample Result#1 Fig: 3 sample output for বর্ণ Figure #3 here user given word is বর্ণ . In our system firstly it goes to dictionary with the বর্ণ. In dictionary it বর্ণ and another word is বর্নন . And it show suggestion two word which your desired word for correction. Sample Result#2 Fig #4: Sample output ড ইলরক্ট In figure #4 user give word is ড ইলরক্ট which is direct found in dictionary and show your given word is correct. Sample Result#3 Fig 5: Sample output for ত োমোরর In figure #5 user write েত ম রর which misspelled add extra character. In our proposed system the input word firstly go to dictionary for matching. When it not found in dictionary it goes to Edit distance, soundex and Metaphone. It gives you the proper desired word like েত ম র. 4.11 Proposed technique code: public partial class FormSoundexMetaphone : Form public FormSoundexMetaphone() InitializeComponent(); textBoxDictionaryPath.ReadOnly = true; btnSearch.Enabled = false; #region Form Events private void Form1_Load(object sender, EventArgs e) cboAlgorithm.SelectedIndex = 0; private void btnSearch_Click(object sender, EventArgs e) // Get list of names to search string[] names</s>
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<s>= txtNames.Text.Split(new char[] { '\r', '\n' }, StringSplitOptions.RemoveEmptyEntries); // List to hold matches List<string> matches = new List<string>(); // Call search method for the selected algorithm string code; if (cboAlgorithm.Text == "Soundex") code = SearchSoundex(txtFind.Text, names, matches); else // Metaphone code = SearchMetaphone(txtFind.Text, names, matches); #region Show result StringBuilder builder = new StringBuilder(); builder.Append("Searching for:\r\n"); builder.AppendFormat("{0} ({1})\r\n\r\n", txtFind.Text, code); if (matches.Count > 0) builder.AppendFormat("Matches found ({0}):\r\n", matches.Count); foreach (string match in matches) builder.AppendFormat("{0}\r\n", match); else builder.Append("No matches found"); MessageBox.Show(builder.ToString()); #endregion private void btnClose_Click(object sender, EventArgs e) Close(); private void buttonBrowse_Click(object sender, EventArgs e) textBoxDictionaryPath.ReadOnly = false; openFileDialog.InitialDirectory = System.IO.Path.GetFullPath(@"..\..\Dictionary"); openFileDialog.Title = "Browse Text Files"; openFileDialog.CheckFileExists = true; openFileDialog.CheckPathExists = true; openFileDialog.DefaultExt = "txt"; openFileDialog.Filter = "Text files (*.txt)|*.txt|All files (*.*)|*.*"; openFileDialog.FilterIndex = 2; openFileDialog.RestoreDirectory = true; openFileDialog.ReadOnlyChecked = true; openFileDialog.ShowReadOnly = true; If (openFileDialog.ShowDialog() == DialogResult.OK) textBoxDictionaryPath.Text = openFileDialog.FileName; textBoxDictionaryPath.ReadOnly = true; String dictionary = File.ReadAllText(textBoxDictionaryPath.Text); List<string> wordList = dictionary.Split('\n', ' ').ToList(); string[] s = wordList.Select(w=> w.Any(x => !char.IsLetter(x)) ? w.Substring(0, w.IndexOf("/")==-1? w.Length: w.IndexOf("/")) : w).ToArray(); txtNames.Lines = s; btnSearch.Enabled = true; #endregion #region Soundex private string SearchSoundex(string find, string[] names, List<string> matches) find = ConvertSoundex(find); // Encode string we want to find string code = Soundex.Encode(find); // Search through the list of names foreach (string name in names) string soundex_name = ConvertSoundex(name); // Compare against soundex-encoded version of name if (Soundex.Encode(soundex_name) == code) // Found a match--add it to list matches.Add(soundex_name); return code; private string ConvertSoundex(string text) StringBuilder engText = new StringBuilder(); for(int index=0; index<text.Length; index++) switch(text[index]) return engText.ToString(); #endregion #region Metaphone private string SearchMetaphone(string find, string[] names, List<string> matches) find = ConvertMetaphone(find); // Encode string we want to find Metaphone metaphone = new Metaphone(); string code = metaphone.Encode(find); // Search through the list of names foreach (string name in names) string metaphone_name = ConvertMetaphone(name); // Compare against soundex-encoded version of name if (metaphone.Encode(metaphone_name) == code) // Found a match--add it to list matches.Add(metaphone_name); return code; private string ConvertMetaphone(string text) StringBuilder engText = new StringBuilder(); for (int index = 0; index < text.Length; index++) switch (text[index]) return engText.ToString(); #endregion CHAPTER V: CONCLUSION We have improved Bangla spelling checking, transliteration and name searching application using Edit Distance, Sondex, Metaphone phonetic encoding. The summary regarding the improvements of our new system is as under: It can be used to develop a spelling checker that can provide the words of same pronunciation in suggestion. It can also be used to develop a transliteration that can be used not only a one to one direct mapping but also be able to give words with same pronunciation from dictionary. It can be used to develop a name searching application as well in which similar sounding names can be easily found in dictionary and ranked in the suggestion. Future research We will try to upgrade the system which will be able to convert the inputted voice into text and as per that text, it will find the related words in the dictonary. If the word exactly matched, it will be displayed directly. Otherwise,</s>
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<s>it will show suggestion for same type of words. This is how the system will be more effective in future. REFERENCES i. https://people.cs.pitt.edu/~kirk/cs1501/Pruhs/Spring2006/assignments/editdistance/Levenshtein%20Distance.htm ii. http://creativyst.com/Doc/Articles/SoundEx1/SoundEx1.htm iii. https://www.codeproject.com/Articles/162790/Fuzzy-String-Matching-with-Edit-Distance iv. https://nlp.stanford.edu/IR-book/html/htmledition/edit-distance-1.html v. https://nickgrattan.wordpress.com/2014/06/21/levenshtein-minimum-edit-distance-in-c/ vi. https://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance\ vii. https://en.wikipedia.org/wiki/Metaphone [1] Definition of phonetic encoding available online at http://www.nist.gov/dads/HTML/phoneticEncoding.html. [2] P Lekho, available online at http://lekho.sourceforge.net/. [3] The Soundex Algorithm, available online at http://www.archives.gov/research_room/genealogy/census/soundex.html. [4] Lawrence Phillips, “Hanging on the Metaphone”, Computer Language, 7(12), 1990. [5] Lawrence Philip’s Metaphone Algorithm, available online at http://aspell.sourceforge.net/metaphone/index.html</s>
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<s>A Hybrid Approach for Transliterated Word-Level LanguageIdentification: CRF with Post Processing HeuristicsSomnath BanerjeeCSE Department,JU,Indias.banerjee1980@gmail.comAniruddha RoyCSE Department,JU,Indiaaniruddha@gmail.comAlapan KuilaCSE Department,JU,Indiaalapan.cse@gmail.comSudip Kumar NaskarCSE Department,JU,Indiasudip.naskar@cse.jdvu.ac.inSivaji BandyopadhyayCSE Department,JU,Indiasivaji_cse@yahoo.comPaolo RossoNLE Lab,UPV,Spainprosso@dsic.upv.esABSTRACTIn this paper, we describe a hybrid approach for word-levellanguage (WLL) identification of Bangla words written inRoman script and mixed with English words as part of ourparticipation in the shared task on transliterated search atForum for Information Retrieval Evaluation (FIRE) in 2014.A CRF based machine learning model and post-processingheuristics are employed for the WLL identification task. Inaddition to language identification, two transliteration sys-tems were built to transliterate detected Bangla words writ-ten in Roman script into native Bangla script. The systemdemonstrated an overall token level language identificationaccuracy of 0.905. The token level Bangla and English lan-guage identification F-scores are 0.899, 0.920 respectively.The two transliteration systems achieved accuracies of 0.062and 0.037. The system presented in this paper resulted inthe best scores across almost all metrics among all the par-ticipating systems for the Bangla-English language pair.Categories and Subject Descriptors1.2.7 [Artificial Intelligence]: Natural Language Pro-cessing, Language parsing and understandingGeneral TermsExperimentation, LanguagesKeywordsWord level language identification, Transliteration1. INTRODUCTIONIn spite of having indigenous scripts, often Indian lan-guages (e.g., Bangla, Hindi, Tamil etc.) are written in Ro-man script for user generated contents (such as blogs andtweets) due to various socio-cultural and technological rea-sons. This process of phonetically representing the words ofPermission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.WOODSTOCK ’97 El Paso, Texas USACopyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$15.00.a language in a nonnative script is called (forward) translit-eration. Especially the use of Roman script in translitera-tion for those languages presents serious challenges to un-derstanding, search and (backward) transliteration. Thesechallenges include handling spelling variations, diphthongs,doubled letters, reoccurring constructions, etc.Language identification for documents is a well-studiednatural language problem [3]. King and Abney[9] presentedthe different aspects of this problem and focussed on theproblem of labeling the language of individual words withina multilingual document. They proposed language identifi-cation at the word level in mixed language documents in-stead of sentence level identification.The last decade has seen the development of transliter-ation systems for Asian languages. Some notable translit-eration systems were built for Chinese [14], Japanese [7],Korean [8], Arabic [1], etc. Transliteration systems werealso developed for Indian languages [6, 16].2. TASK DEFINITIONA query q : < w1w2w3...wn > is written in Roman script.The words, w1, w2, w3, ..., wn, could be standard Englishwords or transliterated from Indian languages (IL), e.g.,Bangla, Hindi, etc. The objective of the task is to iden-tify the words as English or IL depending on whether it isa standard English word or a transliterated IL word. Afterlabeling the words, for each transliterated word, the correcttransliteration has to be provided in the native script (i.e.,the script which is used for writing the IL).</s>
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<s>Names of peo-ple and places in IL should be considered as transliteratedentries, whenever it is a native name. Thus, the systemhas to transliterate the identified native names (e.g. Arund-hati Roy). Non-native names (e.g. Ruskin Bond) should beskipped during labeling and are not evaluated.3. DATASETS AND RESOURCESThis section describes the dataset that have been used inthis work. The training and the test data have been con-structed by using manual and automated techniques andmade available to the task participants by the organizers .The training dataset consists of 800 lines.The testset con-tains 1000 sentences.The following resources provided by the organizers werealso employed:• English word frequency list1: contains standard dictio-nary words along with their frequencies computed from alarge corpus constructed from news corpora.• Bangla word frequency list2: contains Bangla words inRoman script along with their frequencies computed fromthe Anandabazar Patrika news corpus.• Bangla word transliteration pairs dataset [15]: containsBangla-English transliteration pairs collected from differentusers in multiple setups - chat, dictation and other scenarios.4. SYSTEM DESCRIPTIONWe divided the overall task into two sub-problems: (a)word-level language (WLL) classification, and (b) translit-eration of identified IL words into native script.4.1 WLL classification Features4.1.1 Character n-gramsFew studies [9, 5] successfully used the character n-gramfeature and they obtained reasonable results. Therefore, fol-lowing them, we also used this feature from character uni-grams up to five-grams. After empirical study on the devel-opment set, we decided on the maximum length of a wordto be 10 for generating the character n-grams. Therefore, ifthe length of the word is more than 10, then due to the fixedlength vector constraint the system generates 10 unigramsand the last two characters are skipped. Thus the systemalways generates a total of 40 n-grams, i.e., 10 unigrams,9 bigrams, 8 trigrams, 7 four-grams and 6 five-grams. Theentire word is also considered as a feature.4.1.2 Symbol characterA word might start with some symbol, e.g. #, @, etc. Ithas also been observed from the training corpus that symbolsappear within the word itself, e.g. a***a, kankra-r, etc.Sometimes the entire word is built up of a symbol, e.g. “, ?.has symbol(word) =1 if word contains any symbol0 otherwise4.1.3 LinksThis feature is used as a binary feature. If a word is alink, then it is set to 1, otherwise it is set to 0.is link(word) =1 if word is a link0 otherwise4.1.4 Presence of DigitThe use of digit(s) in a word sometimes means differentin the chat dialogue. For example, ‘gr8’ means ‘great’, ‘2’could mean ‘to’ or ‘too’. This feature is also used as binaryfeature. Therefore,has digit(word) =1 if word contains any digit0 otherwise4.1.5 Word suffixAny language dependent feature increases the accuracy ofthe system for a particular language. [2] successfully used1http://cse.iitkgp.ac.in/resgrp/cnerg/qa/fire13translit/English%20-%20Word%20frequencies.txt2http://cse.iitkgp.ac.in/resgrp/cnerg/qa/fire13translit/Bangla-Word%20frequencies.txtthe fixed length suffix feature in the Bangla named entityrecognition task. To include this feature, we have prepareda small suffix-list (10 entries) under human supervision fromthe archive (10 documents) of an online Bangla newspaper.This feature is also used as a binary feature.has suffix(word) =1 if word contains any suffix0 otherwise4.1.6 Contextual ProbabilityThis feature is very much crucial to resolve the ambiguityin the WLL identification problem. Let us</s>
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<s>consider examplesgiven below.• Mama take this badge off of me.• Ami take boli je ami bansdronir kichu agei thaki.The word ‘take’ exists in the English vocabulary. How-ever, the backward transliteration of ‘take’ is a valid Banglaword. Words like ‘take’, ‘are’, ‘pore’, ‘bad’ are truly ambigu-ous words with respect to the WLL identification problemas they are valid English words as well as backward translit-erations of valid Bangla words. In this regard, context of theword can be used to correctly identify the language for suchan ambiguous word. Therefore, we have considered this veryuseful feature.As in the Bangla-English language identification task thelabel should be one from the tag-list: {English, Hindi, Bangla,Others}, we calculate the probability of the previous wordbeing English, Hindi, Bangla and Others. Thus, four prob-abilities have been calculated for the previous word. In asimilar way, the labeling probabilities for the next word havealso been calculated.The system calculates the respective probabilities asPtag(W ) =Ftag(W )F (W ), where, tag is any one from the list: {E,O, H, B}; Ftag(W ) = frequency of the word W belonging totag ; F (W ) = Frequency of word W. These frequencies arecounted from the training corpus. However, for few words inthe testset the respective probabilities are 0. Since we do notwant assign zero probability to those words, we need to as-sign some probability mass to those words using smoothing.We use the simplest smoothing technique, Laplace smooth-ing, which adapts the empirical counts by adding a fixednumber (say, 1) to every count and thus eliminates counts ofzero. For simplicity, we use add-one smoothing. Therefore,the adjusted formula is: Ptag(W ) =Ftag(W ) + 1F (W ) + N, where, N= total number of words in the training corpus.4.2 WLL ClassifierIn this work, Conditional Random Field (CRF) is used tobuild the model for WLL identification classifier. We usedCRF++ toolkit3 which is a simple, customizable, and opensource implementation of CRF.4.3 Post ProcessingAfter CRF classifier labels each word, post-processing heuris-tics are applied to make a rule-based decision over the out-come of the classifier. The following heuristics are employed:Rule-1: Many English words end with ‘ed’ (e.g. decided,reached, arrested, looked, etc.), but we have not found anyoccurrences of any Bangla word ending with that suffix in3http://crfpp.googlecode.com/svn/trunk/doc/index.htmlthe given corpus. Therefore, an word ending with ‘ed’ andhaving no symbol inside it is tagged as an English word. Inthe test corpus we found 306 such occurrences.R1: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘ed’)=true and w 6∈ SWhere, C-Tag(w)=Classifier’s output, H-Tag(w)=Heuristicbased output, has suffix(w, s)= word ends with suffix s, andS = set of special character , E = English tag, B = Banglatag, O = Others tag.Rule-2: An English word may end with ‘ly’ suffix also,e.g. thoughtfully, anxiously, unfriendly, etc. It has been ob-served in the test dataset that few English words were notwritten in correct spelling and they were mis-classified asBangla words, e.g. lvly, xactly, physicaly, etc. These wordsare corrected by applying this rule.R2: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘ly’)=true and w 6∈ SRule-3: It was also</s>
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<s>observed that unlike English words (e.g.evening, kissing, playing, etc.) no Bangla words end with‘ing’ suffix in the training corpus. We found 316 such oc-currences in testset, but some occurrences are not tagged asEnglish because those words start with ‘#’ (e.g. #engineer-ing). This rule was able to correct some spelling errors suchas luking, nthing, njoying, etc.R3: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘ing’)=true and w 6∈ SRule-4: The use of apostrophe s (i.e.,’s) is very commonin English words, e.g. women’s, uncle’s etc. In the testdataset, we found 73 use of it.R4: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘’s’)=true and w 6∈ SRule-5: Another very common use of apostrophe is apos-trophe t (i.e., ’t), e.g., don’t, isn’t, wouldn’t, etc. Even it isused in different way such as rn’t, cudn’t, etc.R5: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘’t’)=true and w 6∈ SRule-6: A few users prefer to use words ending with ’ll, e.g.,I’ll, It’ll, he’ll, you’ll, etc. We found 20 such occurrences inthe test set.R6: H-Tag(w)=E ; if C-Tag(w)= B or O, has suffix(w, ‘’ll’)=true and w 6∈ SRule-7: The use of words like o’clock, O’Keefe etc. are veryuncommon in Bangla social media users. But we found 16such occurrences in the test dataset.R7: H-Tag(w)=E ;if C-Tag(w)= B or O, starts with(w,‘o”)= true and w 6∈ SRule-8: This rule is very much straightforward. If a wordcontains a special symbol, then the word is tagged as O.R8: H-Tag(w)=O ; if C-Tag(w)=B or O or E or H and w∈ SRule-9: Although a few ambiguities are discussed in 4.1.6,there is a high chance of a word being English if it is in theEnglish dictionary. Considering the ambiguity, we also con-sider the probability of the word to be in Bangla language.R9: H-Tag(w)=E ; if C-Tag(w)=B and probability Bangla(w)< 0.08 (this threshold was set empirically.)Rule-10: The use of character repetition in the word is ob-served not only in English and Hindi, but in Bangla as well.The following observations have been noticed:(1) Repetition of a character more than twice at the endof a word has the higher chance of the word being an En-glish/Hindi word than Bangla. E.g. torengeee, plzzzzzz, etc.(2) Repetition of a character more than twice in the mid-dle of a word has the higher chance of the word being aBangla word than English. E.g. kisssob, oneeek, etc.(3) If a word satisfies both condition (1) and (2), then theword is more likely to be an English word. E.g. muuuuaaah-hhhhhhh.The following rules are employed:Case-1: R10a: H-Tag(w) = E ; if C-Tag(w) = B or O orH, end repeat(ch) >= 3 and w 6∈ SCase-2: R10b: H-Tag(w) = B ; if C-Tag(w) = E or O orH, middle repeat(ch) >= 3 and w 6∈ SCase-3: R10c: H-Tag(w) = E ; if C-Tag(w) = B or H orO, end repeat(ch) >= 3 and middle repeat(ch) >= 3 and w6∈ SRule-11: This rule is also very much straightforward. Ifa word contains any substring from the list: {www., http:,https::}, then the word is</s>
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<s>tagged as Others.R11: H-Tag(w) = O ; if C-Tag(w) = B or E or H, andcontains(w) = www.|http:|https::5. TRANSLITERATION SYSTEMFor transliterating the detected Romanized Bangla words,we built our transliteration system based on the state-of-the-art phrase-based statistical machine translation (PB-SMT)model [13] using the Moses toolkit [12]. PB-SMT is a ma-chine translation model; therefore, we adapted the PB-SMTmodel to the transliteration task by translating charactersrather than words as in character-level translation. For char-acter alignment, we used GIZA++ implementation of theIBM word alignment model [4]. To suit the PB-SMT modelto the transliteration task, we do not use the phrase reorder-ing model. The target language model is built on the targetside of the parallel data with Kneser-Ney smoothing [10] us-ing the SRILM tool [11]. The PB-SMT model was trainedon the English-Bangla word transliteration pairs dataset [15]provided by the task organizers. In a bid to simulate syllablelevel transliteration we also built a transliteration model bybreaking the English and Bangla words to chunks of consec-utive characters and trained the transliteration system onthis chunked data. The chunk-level transliteration system issupposed to perform better than the character-level translit-eration system since a chunk contains more context than acharacter. While decoding, we first apply the chunk-leveltransliteration system on the detected Bangla words. If thechunk-level transliteration system is able to transliterate aword only partially (i.e., it still contains roman characters),the untranslated parts are decoded using the character-leveltransliteration system. For breaking the English and Ben-gali words into chunks, we take two approaches. In the firstapproach (Run-1) we simply break words into chunks of con-secutive 2/3 characters. In the other approach (Run-2), webreak words into transliteration units (TU) following theheuristic used in [6]. The TU-level transliteration systemwas trained on named entities.6. RESULTSTable-1 presents the obtained results. Our system achievedan overall accuracy of 0.905 for the language labeling taskwhich is the best among the participating teams.Table 1: ResultsToken level language accuracyLanguage Precision Recall F-MeasureBangla 0.866 0.935 0.899English 0.944 0.899 0.920Token level TransliterationRun Precision Recall F-MeasureRun-1 0.033 0.572 0.062Run-2 0.019 0.338 0.037Other Performance MetricsEQMF All(No Translit.) 0.444EQMF without NE(No Translit.) 0.548EQMF without MIX(No Translit.) 0.444EQMF without NE&MIX(No Translit.) 0.548EQMF All Run-1 0.005EQMF All Run-2 0.004EQMF without NE: Run-1 0.007EQMF without NE: Run-2 0.004EQMF without MIX: Run-1 0.005EQMF without MIX: Run-2 0.004EQMF without NE&MIX: Run-1 0.007EQMF without NE&MIX: Run-2 0.004ETPM: Run-1 227/364ETPM: Run-2 134/364Language Identification Accuracy 0.9056.1 Error AnalysisIt was observed that the WLL classifier based on CRFwrongly predicted due to the small training data. Moreover,some words were predicted correctly by the classifier, how-ever, due to the heuristics the final prediction went wrong;e.g., the word Wannna is re-classified by (R10b) wronglyas Bangla. R10a also mis-classified Hindi words havingcharacter repetition at the end, such as torengeee, Arehhh,etc. R10a also mis-classified Bangla words such as jahhh,jetooooo, etc. Rule-8 re-classified some words due to tok-enization errors in the provided test dataset am!”, back!”,goin’, ekjon-eri, etc. Some words in the testset were of theform word1/word2, such as isharay/nirupay, samanyo/8Betc., which were simply classified as O (i.e., Others) usingRule-8 in our system.The TU-level transliteration system was trained over namedentities; hence it</s>
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<s>performed well for NEs, but the overallperformance was affected because majority of the detectedBangla words were non-NE words.7. CONCLUSIONSIn this paper, we presented a brief overview of our hybridapproach to address the automatic WLL identification prob-lem. We found that the use of simple post-processing heuris-tics enhances the overall performance of the WLL system.Two variants of the transliteration systems were developedbased on the segmentation of the transliteration data, i.e., atchunk-level and syllable-level. As future work we would liketo explore more features for the machine learning model andbetter post-processing heuristics for the WLL identificationtask and try to increase the efficiency of our transliterationsystem.8. ACKNOWLEDGMENTSWe acknowledge the support of the Department of Elec-tronics and Information Technology (DeitY), Government ofIndia, through the project “CLIA System Phase II”.9. REFERENCES[1] Y. Al-Onaizan and K. Knight. Named entitytranslation: Extended abstract. In HLT, pages122–124. Singapore, 2002.[2] S. Banerjee, S. Naskar, and S. Bandyopadhyay.Bengali named entity recognition using margin infusedrelaxed algorithm. In Text, Speech and Dialogue, pages125–132. Springer International Publishing, 2006.[3] K. R. Beesley. Language identifier: A computerprogram for automatic natural-language identificationof on-line text. In American Translators Association,page 54, 1988.[4] P. F. Brown, S. A. D. Pietra, V. J. D. Pietra, andR. L. Mercer. Mercer: The mathematics of statisticalmachine translation: parameter estimation. InComputational Linguistics, pages 263–311, 1993.[5] G. Chittaranjan, Y. Vyas, K. Bali, andM. Choudhury. Word-level language identificationusing crf: Code-switching shared task report of msrindia system. In EMNLP, page 73, 2014.[6] A. Ekbal, S. Naskar, and S. Bandyopadhyay. Amodified joint source channel model for transliteration.In COLING-ACL, pages 191–198. Australia, 2006.[7] I. Goto, N. Kato, N. Uratani, and T. Ehara.Transliteration considering context information basedon the maximum entropy method. In MT-Summit IX,pages 125–132. New Orleans, USA, 2003.[8] S. Y. Jung, S. L. Hong, and E. Paek. An english tokorean transliteration model of extended markovwindow. In COLING, pages 383–389, 2000.[9] B. King and S. Abney. Labeling the languages of wordsin mixed-language documents using weakly supervisedmethods. In NAACL-HLT, pages 1110–1119, 2013.[10] R. Kneser and H. Ney. Improved backing-off form-gram language modeling. In ICASSP, pages181–184. Detroit, MI, 1995.[11] R. Kneser and H. Ney. Srilm-an extensible languagemodeling toolkit. In Intl. Conf. on Spoken LanguageProcessing, pages 901–904, 2002.[12] P. Koehn, H. Hoang, A. Birch, C. Callison-Burch,M. Federico, N. Bertoldi, B. Cowan, W. Shen,C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin,and E. Herbst. Moses: open source toolkit forstatistical machine translation. In ACL, 2007.[13] P. Koehn, F. J. Och, and D. Marcu. Statisticalphrase-based translation. In HLT-NAACL, 2003.[14] H. Li, Z. Min, and J. Su. A joint source-channel modelfor machine transliteration. In ACL, 2004.[15] V. Sowmya, M. Choudhury, K. Bali, T. Dasgupta, andA. Basu. Resource creation for training and testing oftransliteration systems for indian languages. In LREC,2010.[16] H. Surana and A. K. Singh. A more discerning andadaptable multilingual transliteration mechanism forindian languages. In COLING-ACL, pages 64–71.India, 2008.</s>
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<s>J Psycholinguist ResDOI 10.1007/s10936-014-9302-xComputational Modeling of Morphological Effectsin Bangla Visual Word RecognitionTirthankar Dasgupta · Manjira Sinha · Anupam Basu© Springer Science+Business Media New York 2014Abstract In this paper we aim to model the organization and processing of Bangla poly-morphemic words in the mental lexicon. Our objective is to determine whether the mentallexicon accesses a polymorphemic word as a whole or decomposes the word into its con-stituent morphemes and then recognize them accordingly. To address this issue, we adoptedtwo different strategies. First, we conduct a masked priming experiment over native speak-ers. Analysis of reaction time (RT) and error rates indicates that in general, morphologicallyderived words are accessed via decomposition process. Next, based on the collected RT datawe have developed a computational model that can explain the processing phenomena ofthe access and representation of Bangla derivationally suffixed words. In order to do so, wefirst explored the individual roles of different linguistic features of a Bangla morphologi-cally complex word and observed that processing of Bangla morphologically complex wordsdepends upon several factors like, the base and surface word frequency, suffix type/tokenratio, suffix family size and suffix productivity. Accordingly, we have proposed differentfeature models. Finally, we combine these feature models together and came up with a newmodel that takes the advantage of the individual feature models and successfully explain theprocessing phenomena of most of the Bangla morphologically derived words. Our proposedmodel shows an accuracy of around 80 % which outperforms the other related frequencymodels.Keywords Mental lexicon · Morphological decomposition · Masked priming ·Visual word recognition · Frequency effects · Suffix productivityIntroductionThe term mental lexicon refers to the access, representation and processing of the wordsin the human mind and the various associations between them that help fast retrieval andT. Dasgupta (B) · M. Sinha · A. BasuDepartment of Computer Science and Engineering,Indian Institute of Technology, Kharagpur 721302, West Bengal, Indiae-mail: iamtirthankar@gmail.com123J Psycholinguist Rescomprehension of the words in a given context (Aitchison 2005; Marslen-Wilson et al. 1994;Taft and Forster 1975). Words are known to be associated with each other at various levels oflinguistic structures namely, orthography, phonology, morphology and semantics. However,the precise nature of these relations and their interactions are unknown. Understanding theorganization of the mental lexicon is one of the important goals of cognitive science. A clearunderstanding of the structure and the processing mechanism of the mental lexicon will furtherour knowledge of how the human brain processes language. Further, these linguisticallyimportant and interesting questions are also highly significant for computational linguistics(CL) and natural language processing (NLP) applications. Their computational significancearises from the issue of their storage in lexical resources like WordNet (Fellbaum 2010) andraises important questions like, how to store morphologically complex words, in a lexicalresource like WordNet keeping in mind the storage and access efficiency.One of the key issues on which psycholinguists have been investigating for a long time isthe representation and processing of morphologically complex words in the mental lexicon.That is to say, whether for a native speaker, a polymorphemic word like “unpreventable”will be processed as a whole or will it be decomposed into</s>
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<s>its individual morphemes “un-”,“prevent”, and “-able” and finally recognized by the representation of its stem (morphemicmodel). It has been argued that people certainly have the capability of such decompositionsince they can understand novel words like “unsupportable”. However, there has been a longstanding debate whether such decompositions are obligatory (i.e morphemic) or are theyapplicable to only those situations where the whole word access fails (Taft 2004) (partialdecomposition model). An alternative to the morphemic and partial decomposition model isthe full listing model that assumes decomposition is not at all involved and initial processingof words are performed in terms of the whole word representation in the mental lexicon(Burani and Caramazza 1987; Burani and Laudanna 1992; Caramazza et al. 1988). Suchissues are typically addressed by designing appropriate priming experiments (Frost et al.1997; Aitchison 2005) or other lexical decision tasks.Priming results in faster recognition of a stimulus (called the, target) based on the previousexposure of another stimulus (called the prime). Therefore, if the prime and the target wordsare morphologically related (say, MANLY and MAN), then going by the decompositionmodel, as soon as the prime (MANLY) is presented to a subject, it will be decomposedinto its constituent stem (MAN) and the suffix (-LY) and be recognized individually. Thus,recognition of the target word starts well before it is presented to the subject. Naturally, thiswill result in a faster recognition of the target as compared to the case when the target ispreceded by a morphologically unrelated word (say, MOTHER and MAN) where no suchdecomposition of the prime is possible. On the other hand, considering full-listing model,recognition of the target must be independent of the prime. Thus, time to recognize the targetMAN preceded by the prime MANLY must be equal to the case when it is preceded byMOTHER. Hence, if priming by a morphologically related word results in faster recognitionof the target, it may be assumed that decomposition has played its role.The priming experiments can be classified according to the mode of representing theprime and target words: (a) when both are visually presented (Bentin and Feldman 1990;Ambati et al. 2009; Frost et al. 1997; Marslen-Wilson et al. 2008), (b) primes are auditorilypresented but the targets are visually presented (Marslen-Wilson et al. 1994; Marslen-Wilsonand Tyler 1997; Marslen-Wilson and Zhou 1999; Marslen-Wilson et al. 2008), (c) targetsare auditorily presented but the primes are visually presented (Marslen-Wilson et al. 1994).These experiments demonstrate that across the languages, recognition of a target word (sayhappy) is facilitated by a prior exposure of a morphologically related prime word (e.g.,happiness). Since morphological relatedness often implies orthographic, phonological and123J Psycholinguist Ressemantic similarities between two words, several attempts have been made to factor outother priming effects from morphological priming (Bentin and Feldman 1990; Drews andZwitserlood 1995).The masked priming paradigm, where the prime word is placed in between a forward maskand a target word such that it cannot be consciously perceived (Bodner and Masson 1997;Davis and Rastle 2010) also shows some interesting ways of examining morphological effectsin word recognition (Forster and Davis 1984). Through such experiments morphologicalpriming effects are shown</s>
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<s>to exist in the absence of semantic priming for Hebrew (Frost etal. 1997), phonological priming (Crepaldi et al. 2010), and orthographic priming for French(Grainger et al. 1991) and Dutch (Drews and Zwitserlood 1995).A cross modal priming experiment has been conducted for Bangla derivationally suf-fixes words by Dasgupta et al. (2010) where strong priming effects have been observed formorphologically and phonologically related prime-target pairs; weak priming is observedfor morphologically related but phonologically opaque pairs and no priming is observed formorphologically unrelated pairs. Apart from this, we do not know of any other cognitiveexperiments on morphological priming in Bangla or other Indian languages.Several attempts have been made to provide computational models that can predict theprocessing of a given polymorphemic words. The obligatory decomposition model (Taft2004) accounts for the fact that, decomposition of a polymorphemic words depend upon thefrequency of the constituent stem (or the base word). Therefore, higher the stem frequency,easier it is to decompose. On the other hand, the full listing model (Burani and Laudanna1992) states that the access to a polymorphemic word depends upon the frequency of thewhole word. Thus, higher is the surface frequency of a word is, the easier it is to be recognized.The dual route access model (Baayen et al. 1997) argues that whether or not a polymorphemicword will be decomposed into its constituent morpheme, depends upon the surface frequencyof that word; that is, if the frequency of a polymorphemic word crosses a threshold then theword will be accessed as a whole otherwise it will be accessed via its parts.Experiments on English inflected words (Taft and Forster 1975), argued that lexical deci-sion responses of polymorphemic words depends upon the base word frequency. In otherwords, if recognition of a polymorphemic word always takes place through decomposition,then higher the frequency of the stem is (called, base frequency), the shorter is the time torecognize the word (called, Reaction Time or RT). Previous experiments have shown suchbase frequency effects in most of the cases but not for all (Baayen et al. 1997; Bertram etal. 2000; Bradley 1980; Burani and Caramazza 1987; Burani et al. 1984; Colé et al. 1989;Schreuder and Baayen 1997; Taft and Forster 1975; Taft 2004).Later, the dual processing race model (Baayen 2000) was proposed where both the full-listing and morphemic path compete among each other and depending upon the frequencyof base and the surface word any one of the paths are chosen. The model proposes a specificmorphologically complex form is accessed via its parts if the frequency of that word is abovea certain threshold of frequency, then the direct route will win, and the word will be accessedas a whole. If it is below that same threshold of frequency, the parsing route will win, and theword will be accessed via its parts. However, what the dual processing model fails to explainis whether the stem frequency of a derived word is also involved during the recognitionprocess.The obligatory decomposition (morphemic) model has been proposed by Taft (2004)for inflectional suffixed English words and showed that stem frequency of a word plays</s>
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<s>animportant role during decomposition of a derived word. Further, it argued that access to apolymorphemic word always takes place via two phases, a) decomposition and b) recom-bination. Therefore, during recognition, any polymorphemic word will be first decomposed123J Psycholinguist Resinto its constituent morphemes where the morphemes will be individually recognized andthen in the combination phase they will be recombined together to recognize the whole word.The effect of morphological family size was observed by Schreuder and Baayen (1997).It has been shown that the response latencies of morphologically complex words in Englishsignificantly depend on the morphological family size of the word in concerned. Similarobservations have been made in the works of Baayen et al. (2006), Pylkkänen et al. (2004),Jong et al. (2000), Carlisle and Katz (2006), Bertram et al. (2000), Schreuder and Baayen(1997). Closer to the present scope, by Prado et al. (2005) models the paradigmatic structureof a morphologically complex word. The work describes a distributed connectionist modelof visual word recognition that explores how the paradigmatic effect can describe the lexicaldecision tasks of complex words. Milin et al. (2009) also studied the paradigmatic effect ofmorphologically complex words through information theoretic approach. Here, thereactiontime of a complex word has been modeled based on the entropy of that word. Ford et al. (2010)in their work analyzed the role of stem and suffix family size. They observed both the stemas well as the suffix family sizes plays important role in the recognition of morphologicallycomplex words.In spite of the plethora of work that has been done to understand the representationand processing of polymorphemic words in the mental lexicon, a coherent picture is yet tobe emerged. Further, most of the studies reported so far conducted experiments mainly inEnglish, Hebrew, Italian, French, Dutch, and few other languages (Frost et al. 1997; Forsterand Davis 1984; Grainger et al. 1991; Drews and Zwitserlood 1995; Taft and Forster 1975;Taft 2004). However, we do not know of any such investigations for Indian languages, whichare considered to be morphologically richer than many of their Indo-European cousins. Onthe other hand, several cross-linguistic experiments indicate that mental representation andprocessing of polymorphemic words are language dependent (Taft 2004). Therefore, thefindings from experiments in one language cannot be generalized to all languages. Hence,it is important to conduct similar experimentations in other languages. Bangla, in particular,supports stacking of inflectional suffixes, a rich derivational morphology inherited fromSanskrit and some borrowed from Persian and English, and an abundance of compounding,as well as mild agglutination.Accordingly, the objective of this paper is to present computational models that can beused to understand the organization and processing of Bangla derivationally suffixed poly-morphemic words in the mental lexicon. Our aim is to determine whether the mental lexiconprocesses Bangla morphologically complex words in terms of full-listing, morphemic orpartial decomposition model. For this, we first conducted the masked priming experimentover a set of 500 Bangla morphologically complex words and collected reaction time datafrom 28 subjects. The experimental result shows that priming behavior is observed only forthose cases where the prime is the derived form of the target and</s>
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<s>having a recognizablesuffix (like, sonAli–sonA (GOLDEN–GOLD), and bayaska–bayasa (AGED–AGE). Weakpriming is observed for cases where the prime is a derived form of the target but do not havea recognizable suffix (like, sabhAba (HABIT)–sbAbhAbika (NATURAL))or when the primeand the target are not morphologically related at all but have a recognizable suffix (like,AmadAni (IMPORT)–Ama (MANGO)). These observations initially indicate the obligatorydecomposition model proposed by Taft and Forster (1975), Taft (2004) that assumes poly-morphemic words to be processed via decomposition. Further analysis of the RTs obtained inthe experiments indicate that processing of Bangla polymorphemic words may be achievedby the dual route decomposition model as proposed by Baayen (2000). However, contraryto the idea of considering base and/or surface frequency as sole predictor of processingBangla polymorphemic words in the mental lexicon, we have explored the individual roles of123J Psycholinguist Resdifferent features of a morphologically complex words like, the relative frequency betweenthe base and surface word, type-token ratios, and role of suffixes (their family size, type-tokenratio, and productivity) in morphological decomposability. Accordingly we have proposeddifferent models. We have evaluated the proposed models with the results obtained from thepriming experiment. Finally, we combine the role of all these characteristics and develop amore robust computational model that can predict the organization and processing of Banglamorphologically complex words. We have evaluated our proposed model with derivationallysuffixed Bangla words and found that the performance of our proposed model outperformsthe performance of the existing ones.The rest of the paper is organized as follows: section “Psycholinguistic Study of BanglaPolymorphemic Words through Masked Priming Experiments” presents related works; sec-tion “Applying Frequency Models to Bangla Polymorphemic Words” describes the maskedpriming experiment performed over a set of Bangla morphologically complex words. Sec-tion “Model 3: Relative Frequency between Base and the Derived Words” describes differentfrequency based models and their performance in predicting the processing mechanismsof Bangla polymorphemic words; section “Exploring the Role of Suffixes in Processing ofBangla Words” describes the newer models of word recognition; sect. “Model-6: CombiningModel-3, Model-4, and Model-5” concludes the paper by summarizing the observations anddiscusses the findings.Psycholinguistic Study of Bangla Polymorphemic Words through Masked PrimingExperimentsIn order to study the effect of priming on morphologically derived words in Bangla, weexecute the masked priming experiment as discussed in Forster and Davis (1984), Rastleet al. (2000), Marslen-Wilson et al. (2008) for Bangla derivationally suffixed words. In thistechnique the prime is placed between a forward pattern mask and the target stimulus, whichacts as a backward mask. This is illustrated below.Mask (500 ms) ########Prime (72 ms) sonAli (GOLDEN)Target (500 ms) sonA (GOLD)The prime and the target words are either morphologically and/or semantically relatedor orthographically transparent to each other. A pair of word is said to be morphologicallyrelated if they meet the following conditions:a) One word is the derived form of the otherb) The derived form has a recognizable suffixFor example, the word pairs bADiOyAlA (House keeper) and bADi (House) are morphologi-cally related since, bADiOyAlA is derived from bADi and has a recognizable suffix -OyAlA.A pair of word is said to be orthographically transparent if whole or a</s>
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<s>significant part of oneword is fully or partly contained in the other word. Orthographically transparent words mayor may not be morphologically or semantically related to each other. For example, maShA(mosquito) and maShAla (flame) are orthographically transparent but morphologically notwhereas, our previous example, bADIOyAlA (House Master) and bADi (House) are bothorthographically transparent and morphologically related.After presenting the target probe, the subjects were asked to make a lexical decisionwhether the given target is a valid word in that language. The same target word is again123J Psycholinguist Resprobed after a random amount of time, but with a different visual probe called the controlword. The control words do not have any morphological, orthographic or semantic relatednesswith the target. For example, baYaska (aged) and baYasa (age) is a prime-target pair, for whichthe corresponding control-target pair could be naYana (eye) and baYasa (age).1The time taken by a subject to complete the lexical decision task after the visual presen-tation of the target is defined as the response time (RT). The RTs between a prime-targetand the corresponding control-target pair are compared to identify whether there is enoughevidence of morphologically structured lexical representation. Experiments in English andother languages show that in general the RT between the prime-target pair is significantlyless than that of the control-target pair, implying the presence of morphological primingeffect. Nevertheless, all linguistically apparent morphological processes need not have equalpriming effects or any effect at all.Materials and MethodsWe selected 500 prime-target pairs, where the primes are related to the targets either in termsof morphology, semantics and/or orthography. In order to factor out the effects of semantics ororthography, we adopted the same technique discussed in Rastle et al. (2000), Marslen-Wilsonet al. (2008) and classified the words into five different classes each consists of 100 wordpairs. Words in these classes are classified according to their morphological, semantic andorthographical relationship. For example, class-I words or [M+S+O+] consists of word pairsthat are morphologically (M+), semantically (S+) as well as orthographically (O+) related.Here, the “+” (as in M+) indicates relatedness and “−” indicates unrelatedness. Similarly,words that are morphologically unrelated but orthographically related will be represented as[M−S−O+] and so on. We also introduces a special class of words [M’+S−O+] which aresimilar to the word class [M−S−O+], however, this words consists of a valid and transparentBangla suffixes. For example, words like, AmadAni(import) consists of a valid Bangla suffix(-dAni) and a valid stem Ama(Mango). However, AmadAni and Ama does not have anymorphological connection among them. These classes of words have been introduced toobserve the priming phenomena for pseudo suffixed words. Table 2 describes these fiveclasses with examples.It is interesting to note that while it is very easy to collect word pairs belonging to classI, it is hard to come up with morphologically derived word forms in Bangla which areorthographically unrelated. In fact, almost all the native Bangla suffixes (e.g., -A, -I, -li, -oYA)do not change the form of the root to which it attaches. However, there are some derivationalprocesses inherited from Sanskrit, where the root forms are phonologically distinct from thederived ones, e.g., hatyA (to</s>
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<s>kill)–hi.nsA (violence, i.e., desire to kill).For each of the 500 target words, we selected another set of 500 control words. Thesecontrol words are similar to the prime words in terms of word length, and number of syllables.However, they are neither morphologically related nor orthographically transparent to thetargets. Some statistics about the prime, target, and control words are presented in Table 1.As discussed earlier, after hearing the auditory prime, a visual probe is presented to thesubjects based on which some lexical decision have to be made. Thus, it is essential to restrictthe subjects to make any strategic guess about the relationship between prime and the targetword pairs. This can be achieved by introducing some filler in between the actual prime-targetor control-target pairs. We constructed a set of 500 filler pairs which can be categorized into1 This study follows the experiment 1 of Rastle et al. (2000); however, for the sake of readability we brieflydescribe the design process and other details.123J Psycholinguist ResTable 1 Statistics of the target, prime and control wordsWord type Avg. word length Avg. no. of complex characters Avg. corpus frequencyTarget 4.0 (2.0) 0.260 (0.11) 32.63 (10.10)Prime 6.4 (1.9) 0.464 (0.29) 25.82 (7.04)Control 6.2 (1.2) 0.472 (0.12) 25.14 (8.33)Number in parenthesis signifies standard deviationsthe following five sets of 100 word pairs each: (a) where the prime is a valid word but thetarget is not, although it is orthographically contained in the prime and is obtained by deletingsome word final character-string, e.g., kapAla (fore-head)–kapA (non-word); (b) where thetarget is a valid word but the prime is not, although it orthographically contains the targetand is derived by adding a suffix to the target, e.g., hAtAri (non-word)–hAta (hand); wherethe prime is valid but the target is not, and is obtained by swapping the individual alphabetsof the target, e.g., pAgalAmo (madness)–pAlaga (non-word); and (c) where both the primeand target are valid words without any morphological and phonological relatedness.Thus, all together, there are 1,500 word pairs including 500 prime-target pairs, 500 control-target pairs and 500 fillers. Before presenting the word pairs to each subject, they are ran-domized and divided into two set, such that the prime-target pair and the correspondingcontrol-target pair are present in different sets. Moreover, each set contains exactly half ofthe prime-target and half of the control-target pairs.ProcedureThe experiment was conducted using the DMDX software tool.2 Corresponding to eachvisual probe, subjects had 3,000 ms to perform the lexical decision after which the systempresents the next masked prime followed by a visual stimulus. The subject performs thedecision task by pressing either the “K” button (for valid word) or the “S” button (for invalidword) of a standard QWERTY keyboard. The system automatically records the reaction time(RT), which in this case is the time between the onset of the visual probe and pressing of oneof the keys by the subject.Before starting the real experiment all the subjects were given a short training aboutthe task. A trial run was also performed using the separately collected 20 trial word pairs.As discussed earlier, the experiment is divided into</s>
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<s>five different phases. The experimentalprocedure for both the phases is same except that the prime and control words are different.The duration of each phase is about 25 min. Since a continuous session of 25 min require alot of attention and is tiring for the subjects, we further divided each phase of the experimentinto five small sessions of five minutes each. There was a break for ten minutes between thesessions.ParticipantsThe experiments were conducted on 32 highly educated native Bangla speakers; 27 of themhave a graduate degree and 5 hold a post graduate degree. The age of the subjects variesbetween 22 and 35 years (Table 2).2 http://www.u.arizona.edu/~kforster/dmdx/dmdx.htm.123http://www.u.arizona.edu/~kforster/dmdx/dmdx.htmJ Psycholinguist ResTable 2 Dataset for the experimentClass Explanation ExamplesM+S+O+ Morphologically derived, stem and suffix aretransparent and decomposable,semantically and orthographically relatednibAsa (residence)-nibAsi (resident)M+S+O− Morphologically derived, stem and suffix areopaque, semantically related butorthographically notmitra (friend)-maitri (friendship)M’+S−O+ Morphologically unrelated, transparent stemand suffix, semantically unrelated butorthographically relatedAma (Mango)-AmadAni (import)M−S+O− Semantically related but Morphologicallyand Orthographically unrelatedjantu (Animal)-bAgha (Tiger)M−S−O+ Morphologically and semantically unrelatedbut orthographically relatedghaDi (watch)-ghaDiYAla(crocodile)ResultsThe RTs with extreme values and those for incorrect lexical decisions (about 1.8 %) wereexcluded from the data.3 We have also discarded one prime-target pair from our dataset dueto its incorrect spelling. Further, four subjects have to be excluded from the experiment dueto their inconsistent and extremely high error rates. Overall, we have analyzed the RTs of 490prime-target and 490 control-target pairs for a total of 28 subjects. Table 3 summarizes theaverage RTs for the prime and control sets for the five classes. The RT and error rate data weresubmitted to by-subject and by-item analyses of variance with the following main factors:priming relation (prime vs. control) and relation classes (M+S+O+, M+S+O−, M’+S−O+,M-S+O−, and M−S−O+).We observed that, overall, the average RTs for Bangla control-target pairs are morethan the corresponding prime-target ones. In other words, priming relation had a signif-icant effect over the control relations. We have computed the by subject and by item F-scores as F1 (1, 23) = 32.42, p < .002; F2 (1, 485) = 48.93, p < .005. “Correct”responses to targets were faster when they appeared after the primes than unrelated con-trols. The priming effects of individual classes along with their significance values aredepicted in Table 3. To summarize, strong priming effects are observed when the targetword is morphologically derived and has a recognizable suffix, semantically and ortho-graphically related with respect to the prime[M+S+O+] (F1(1, 23) = 18.21, p = 0.001,F2(1, 96) = 21.13, p < 0.03); although statistically significant, but week priming isobserved for word pairs belonging to [M+S+O−], and [M’+S−O+]; no priming effectsare observed when the prime and target words are orthographically related but share nomorphological or semantic relationship [M−S−O+] (F1(1, 23) = 17.34, p = 0.006,F2(1, 96) = 13.47, p < 0.004) or only semantically related but without any morpholog-ical or orthographic relation[M−S+O−]. These results thus rules out the possibility thatpriming in [M+S+O+] could be due to individual effects of orthographical or semantic relat-edness.3 Any RT value that falls outside the range of Average RT 500 ms is considered as extreme.123J</s>
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<s>Psycholinguist ResTable 3 Average RT for the word classes, the F-Score and p valuesClass Avg. RT (in ms) and error rates (in %) ANOVAPrime Error Control Error Diff F-score p value[M+S+O+] 523 2.40 589 1.20 66 F1(1, 23) = 18.21 p < 0.001F2(1, 96) = 21.13 p < 0.030[M+S+O−] 653 2.00 660 1.60 7 F1(1, 23) = 10.04 p = 0.07F2(1, 94) = 13.13 p < 0.06[M’+S−O+] 554 2.49 542 1.86 12 F1(1, 23) = 12.42 p < 0.009F2(1, 94) = 11.93 p < 0.040[M−S+O−] 606 3.12 597 2.11 −19 F1(1, 23) = 17.56 p < 0.02F2(1, 96) = 18.39 p < 0.005[M−S−O+] 690 3.69 657 3.64 −43 F1(1, 23) = 19.67 p = 0.001F2(1, 95) = 15.53 p < 0.008Analysis of RTs for Lexical ItemsIt is interesting to look at the individual lexical items whose priming behavior deviatesfrom that of their class. For instance, akarma (useless work)–akarmaNyA (worthless girl),pAkA (smart)–pAkAmo (street smartness), srama (labour)–sramika (worker) and kShamA(forgiveness)–kShamaNIYa (forgivable) exhibit the least priming effect in [M+S+O+].In [M+S+O−] class, prime-target pairs like, pAna (to drink)–pipAsA (thirsty), dharA(hold)–dhairya (patience) and chalA (move)–chAlita (controlled) show no priming effectdespite there is a strong morphological association between the prime-target pairs. In general,we observe that participants are unable to recognize the morphological connection betweenmost of the derivationally suffixed word pairs in the [M+S+O−] class. Examples includesuhRRida (friend)–souhArdya (friendship), uchit (appropriate)–auchitya (appropriateness)and hatyA (murder)–hi.nsA (violence). One explanation for this is, Bangla inherits thesemorphological forms from Sanskrit and the derivational process is unknown.Another important observation from the experiment is that a significant number (around38 %) of prime-target pairs belonging to the [M+S+O+] class shows weak or no prim-ing despite their high morphological association. For example, pairs like, ghana (dense)–ghanatba (density) and ga ∼ Nga (river Ganges) – ga ∼ Ngajala (water from Ganges),jiba (living being)–jibanta (alive), chora (thief)–chorAI (smuggled) etc. shows very weakpriming effect. In order to eliminate possible experimental errors, we repeated the same prim-ing experiment with these words to the same set of subjects and obtained the same result forall the pairs (although the average RT for some of the target deviates from that of the originalresult but this did not change the overall results).Analysis of High RT Lexical ItemsWe also observed that the RTs for certain pair of words were significantly higher than whatone would expect and consistently so across all the participants. Manual inspection of thesewords indicates that the target or the corresponding prime/control words in such cases haveone of the following properties:123J Psycholinguist ResTable 4 List of Bangla wordshaving conjugate characters andtheir average RT across 28subjectsNumber inside parenthesissignifies standard deviationsWord Corpus Frequency Length Avg. RTjIbanta(alive) 26 6 641 (67)bayaska(old) 34 6 773 (73)hindustAna(India) 1 11 754 (98)ghanatba(density) 6 5 774 (76)(sUryAsta)(sunset) 20 9 846 (53)kerAnigiri(clarkship) 8 10 1,078 (102)lambAi(length) 1 6 1,132 (111)rAShTrIYa(national) 113 9 1,227 (94)a) Very infrequentb) Long in terms of the number of characters present (>7)c) Presence of certain conjugates such as (Sh+T), (l + p) and (∼ N +g), and other irregularor non-transparent glyphs (g + u) and (h+RRi)</s>
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<s>in the targetd) Incorrect spelling of the target (e.g., sharira instead of sharIra)Frequency effects on recognition time are well studied (Forster and Davis 1984; Taft 2004)and explain observation (a). It is quite well known that visual word recognition time andaccuracy depends on several factors such as, font size, font type, eccentricity, i.e., the angleof the visually represented word from the focus of the eye, and the crowding effect, i.e.,the physical length of a word [see, e.g., Jo (2000)]. Therefore, observation (b) is also notsurprising. However, the last two observations are specific to Bangla orthography and throwup some interesting research questions.The Bangla script uses a large number of non-transparent glyphs for conjugates andalso some consonant-vowel pairs. These glyphs have been a point of discussion amongstthe scholars of Bangla language, especially for pedagogical reasons: non-transparency incharacter representation leads to poor recognition and recall of the glyphs as well as the wordscontaining them; this negatively affects the learning process in young children. Therefore,there have been proposals for using the less common but easy to recognize transparent formsof these glyphs. We do not know of any systematic study that explores and quantifies thecognitive load associated with the learning and processing of the glyphs with varying degreeof transparency. Since such a study is beyond the scope of the current work, the experimentalitems were not prepared to specifically identify glyph recognition complexities. Nevertheless,we do observe an effect of glyph transparency and glyph usage frequency on word recognitiontime. Uncommon and non-transparent glyphs (e.g., (Sh+T)) have highest recognition time,whereas very frequent glyphs (e.g., (k+Sh)), even if non-transparent do not seem to have anegative effect on the recognition time of the words. Table 4 depicts a list of Bangla wordscontaining different conjugate characters and their average RT over the 28 subjects.High recognition time and error for incorrect spellings, or non-words, is a well-knownfact. However, it is interesting in the context of Bangla because Bangla does not distinguishbetween short and long vowels in pronunciation, even though the distinctions are traditionallymaintained in the written forms. Recently, there have been several controversial proposalsfor spelling reforms where all long vowels are to be replaced by their shorter counterparts.The unintentional error in our dataset, sharira (body) instead of the more commonly foundand popularly acceptable form sharIra, was accidentally discovered when we observed veryhigh RT for the pairs involving this item as the target. Thus, it might be argued that speakerswho have learnt the traditional spellings will find it hard to recognize their new spellings.123J Psycholinguist ResTable 5 Comparison of RTs between Bangla words with their conventional and un-conventional spellingforms. Number inside parenthesis signifies standard deviationsIn order to extend this argument we have conducted a separate lexical decision experiment.Here, we chose 80 Bangla words that have different accepted spelling conventions. The wordswere shown to 21 subjects using the procedure as discussed in Baayen et al. (1997), Taft(2004). We asked subjects to recognize whether a given word is valid or not. Similar to thepriming experiment discussed above, we have recorded the reaction time of individual wordsper</s>
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<s>subject. An illustration of some typical Bangla words and their average RT is depictedin Table 5. We found that, for most of the cases the RT of those words that exhibits a morecommon form of representation is significantly lower than the words having an uncommonrepresentation F1(1, 20) = 11.4, p < 0.05; F2(1, 80) = 23.11, p < 0.02. This is not asurprising conclusion, though the exact nature and extent of difficulty in perceiving the newforms is a topic of further research.Analysis of Error RatesDuring priming experiments, participants can make an incorrect lexical decision on whethera word is valid or invalid. The errors could be due a participant’s incorrect judgment aboutvalidity of a word or a wrong selection made despite of a correct judgment. In general, it hasbeen observed that error rates and RT for non-words are higher than valid words. Table 6reports the error rates and RT for the prime-target, control-target and the fillers. As expected,we observe high error rates and high RT for fillers, which mostly consist of non-words astarget or prime. In fact 81 % of the total errors for the fillers are for the non-words. The overallerror rate, however, is quite low.Recall that test of significance for individual subjects revealed 28 out of 32 participantsshowed statistically significant priming effects (p < 0.03), which led us to hypothesize thatthe remaining four participants were not paying good attention during the experiments or arenot well exposed to Bangla due to their educational medium.123J Psycholinguist ResTable 6 Comparison of the RTand error rates between prime,control and fillersClass Average RT (m.sec) Error (%)Prime 579 1.2Control 654 1.9Fillers 1,011 6.2Fig. 1 Comparison of error rates across word classesFig. 2 Comparison of error ratesfor different categories of lexicalitems. The gray and the whitecells are respectively forparticipants who displayedsignificant and insignificantpriming effectsTherefore, we would expect their error rates to be higher than that of the other 28 partici-pants. Figure 1 plots the histogram of error rates for the significantly primed (left bars) andnon-significantly primed participants. Overall error rate of the former class of participants(41 %) is much less than that of the latter (59 %), which matches our speculation. Again,as one would expect, the maximum errors are made for fillers. Among the valid words, thehighest error rates are observed for the class [M−S+O−] and [M−S−O+] (see Fig. 2). Recallthat these are the classes for which we do not observe any priming effect.123J Psycholinguist ResDiscussionAs explained earlier, the effect of priming with a morphologically derived word vindicatesdecomposition, leading to reduced RT of the target. However, it is apparent from the aboveresults that all polymorphemic words do not decompose during processing. This contradictsthe obligatory decomposition model of Taft and Forster (1975), Taft (2004). Naturally, thequestion that arises is what are the other factors that are responsible for the decompositionof Bangla polymorphemic words? In order to answer this we need to further investigate theprocessing phenomena of Bangla derived words. One notable means is to identify whether thestem or suffix frequency of a polymorphemic word is involved in</s>
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<s>the processing stage of thatword. For this, we apply the existing frequency based models to the Bangla polymorphemicwords and try to evaluate their performance by comparing their predicted results with theresult obtained through the priming experiment.Applying Frequency Models to Bangla Polymorphemic WordsModel-1: Base Word Frequency EffectsThe base word frequency model states that the probability of decomposition of a Banglapolymorphemic word depends upon the frequency of its constituent stem. Thus, a polymor-phemic word that constitutes a high frequency stem will be decomposed faster than a wordhaving low stem frequency. In order to compare the results with respect to that of the maskedpriming experiment discussed in the previous section, we made a slight change to the origi-nal model. We propose that if the stem frequency of a polymorphemic word crosses a giventhreshold value τ , then the word will decomposed into its constituent morpheme. The modelis formally represented as:Decomposabili t y (w) =TRUE, i f log10 ( f requency (Wstem)) ≥ τFALSE, i f log10( f requency(Wstem)) ≤ τThe value of τ is computed as the log of average base word frequency of Bangla words from acorpus4. This returns the value of τ as 0.09. We apply model-1 to a set of 500 morphologicallyderived words. According to model-1, words like, pathika (318),5 jalA (15), bADiwAlA (19),and baYaska (34) will be decomposed into their constituent stem and suffixes during theprocessing stage. The reason behind this is that, all these words are derived from very highfrequency stems like, patha (2241), jala (1736), and bADi (1118). Thus, priming phenomenawill be observed if these stems (considered as targets) are preceded by the derived words (i.ethe primes). Since, prior exposure of the prime will result in decomposition of the derivedprime word into its morphemes and thus the recognition of the target will start well beforethe actual target is probed. Similarly, according to model-1, derived words like ginnipanA,rAjakIYa, and nibAsi will not be primed and thus not be decomposed during the processingstage of the Bangla polymorphemic words. The predicted values of the model are evaluatedwith respect to the results obtained from the priming experiment discussed in section. Theperformance of the model is computed in terms of Precision, Recall, F-Measure and Accuracy.The confusion matrix along with the computed results is depicted in Table 7. We observed4 Corpus frequency is computed by combining the CIIL, and Anandabazar corpus and literary works ofRabindranath Tagore, and Bankim Chandra available from (www.ciil.org, iitkgp.ernet.in and nltr.org).5 Number in the parenthesis represents the frequency of a word in the corpus.123http://www.ciil.orghttp://iitkgp.ernet.inhttp://nltr.orgJ Psycholinguist ResTable 7 Summarizing the resultsof base word frequency modelModel-1: Baseword frequency(BF) (values out of500 words)PerformanceFalse positive 135 Precision (%) 60True negative 111 Recall (%) 78True positive 199 F-Measure (%) 68False negative 56 Accuracy (%) 62that the model possess an accuracy of 62 %. However, from the Table 4 we observe thefalse positive and false negative values to be around 26 and 11 % respectively. This indicatesfor these 26 % of the words, the base word frequency model predicts no morphologicaldecomposition due to extremely low</s>
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<s>base word frequency (ranges between 1 and 7 out of4 million) but the priming experiment shows high degree of morphological decomposition.Similarly, for the On the other hand, for about 11 % of the word the model fails to explain whyaround 26 % (like, ekShatama, juYADi and rAjakiYa) words having extremely low base wordfrequency (ranges between 1 and 7)shows high degree of priming. Moreover, the model alsofails to explain the negative decomposability of 11 % words (like, laThiYAla, dAktArakhAnA,and Alokita) despite having high root word frequencies (ranges between 100 and 1,100).Hence, in the next section we proceed to experiment with the derived word frequency modelto get a better model that can be used to explain the above exceptions.Model-2: Derived Word Frequency EffectIn this model we try to validate the priming phenomena with respect to the whole wordfrequency. The hypothesis is that, if a specific morphologically complex form is above acertain threshold of frequency, then the whole word access will be preferred instead ofdecomposition model, and thus no priming effect will be visible in this case. On the otherhand if the derived word frequency is below that same threshold of frequency, the parsingroute will be preferred, and the word will be accessed via its parts. The derived word frequencymodel can be formally represented as:Decomposabili t y (w) =TRUE, i f log10 ( f requency (w)) ≤ τFALSE, i f log10( f requency(w)) ≥ τIn order to apply this model to Bangla polymorphemic words, we have computed the thresholdvalue to be the average corpus frequency of words which comes out to be 1.33. Therefore,a Bangla morphologically complex word whose surface frequency exceeds the thresholdlimit of τ will be accessed as a whole otherwise; it will be decomposed into its parts. Forexample, words like sonAli(179), galAbAji (334), and suryAsta (407) must be processedas a whole and words like, ginnipanA, juYA.Di, and ekaShatama will be parsed into theirconstituent morphemes namely ginni, juYA, and ekaSha. Similar to the approach discussedin model-1, the same 500 polymorphemic words were given as an input to the model. Thepredicted values of the model are then compared with the actual data collected from thepriming experiment (see Table 8 for the confusion matrix along with the computed results).From the results depicted at Table 8, we observe that the model can be used to explain thepossible decomposition of low frequency derived words (like, juYA.Di, nishThAbAna, andekaShatama) which model-1 fails to explain. Thus, the false positive value for the presentmodel is lower than that of model-1 (21 %). However, model-2 performs poorly due to the123J Psycholinguist ResTable 8 Summarizing the resultsof surface word frequency modelModel-2: surfaceword frequencymodel (SF) (valuesout of 500 words)PerformanceFalse positive 111 Precision (%) 58True negative 88 Recall (%) 51True positive 155 F-Measure (%) 54False negative 143 Accuracy (%) 49high false negative value (28 %). This implies the model fails to recognize the potentiallydecomposable words (like, meghalA, pAkAmo and AkAShamandala) properly.DiscussionFrom the above results we observe that, Model-1 predicts that the priming/decompositionwill take place if the base word frequency is high, irrespective</s>
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<s>of the frequency of the prime.However, the prediction of the model was not validated when the prime as well as thetarget words are both having high frequency. On the other hand, Model-2 predicts that prim-ing/decomposition will take place if the prime is of low frequency. However, the model wasnot validated from the experimental results for low frequency prime and low frequent targetpairs. Hence, the two extremes of paring call for a newer model.Model 3: Relative Frequency between Base and the Derived WordsIn a pursuit towards an extended model, we combine the model 1 and 2 together to observe ifand how their combination can predict the parsing phenomena. One way to combine the baseand derived word frequency is through regression analysis. In accordance to the techniquediscussed in Hay and Baayen (2001), we took the log of frequency of both the base and thederived words and plotted their values in a log-log scale. In order to get the best-fit curveover the given dataset we have used the least square fit regression method, the equation ofthe straight line being:log10(BaseFrequency) = 0.346 × log10(Sur f aceFrequency) + 1.611We propose that any point that falls above the regression line will be parsed into its constituentmorphemes during processing. On the other hand, points situated below the regression linewill be accessed as a whole. In other words, given the surface frequency of a derived word W,the equation above can predict the frequency of the corresponding base word. If the predictedfrequency of the base word is greater than the actual frequency of the Base word then thepoint lies above the regression line and thus, during processing these words will be accessedvia the decomposition model. This is depicted in Fig. 3 which illustrates the surface and baseword frequency distribution of 2,000 Bangla polymorphemic words. The model predicts thatthose points that lie on or above the regression line will be parsed during processing whereaspoints lying below the regression line will be accessed as a whole. The results are depictedin Table 9. We observe that the model performs much better (with false negative and falsepositive values below 17 %) than the previous two models.123J Psycholinguist ResFig. 3 The relation between log derived frequency and log base frequency for 2,000 different Bangla poly-morphemic words. Solid line represents least squares fit regression lineTable 9 Summarizing the resultsof relative frequency modelModel-3: base andsurface wordfrequency ratio(values out of 500words)PerformanceFalse positive 88 Precision (%) 70True negative 143 Recall (%) 75True positive 199 F-Measure (%) 72False negative 67 Accuracy (%) 69We validate our model by comparing its predicted results with the results obtained fromthe masked priming experiment on 500 Bangla polymorphemic words. The results of thepredicted values of the model along with accuracy are depicted in Table 9. The present modelshows an accuracy of 69 %. Consequently a significantly high number of words (31 %) arewrongly classified by the present model. This may be accounted for by the fact that most ofthe derived words that could not be correctly classified by the present model are composed oflow</s>
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<s>frequency stem and suffixes. This led us to further modify the existing model to study therole of individual suffixes during the morphological decomposition of Bangla polymorphemicwords.Exploring the Role of Suffixes in Processing of Bangla WordsOne of the key issues that have not been addressed in Model-3 is the fact that whetherthe regression analysis between base frequencies on derived frequency across suffixes willgenerate any variation in the slope and intercept of the resulting line. It has been observedthat, for English, regressing between base and derived word frequency generates different123J Psycholinguist ResFig. 4 The relation between log derived frequency and log base frequency for four affixes. The lines representleast squares regression linesslope and intercept values. Hay and Baayen (2001) showed that suffixes belonging to highintercept values shows higher tendency to decompose than suffixes with low intercept values.In this section, we would like to examine the same for Bangla. Therefore, we will tryto examine whether the regression analysis between base and derived frequency of Banglawords varies between suffixes and how these variations affect word decomposition. For this,we choose six different Bangla native suffixes with varying degree of token frequencies. Foreach suffix, we choose 10 different derived words. Figure 4 illustrates the chosen suffixescorresponding to different suffix classes and their base word and derived word frequencies.Finally, we plot the regression line between words under each suffix and found that theintercept of the regression line for Bangla suffix shows considerable variation.6 Figure 4illustrates the regression analysis of the six different Bangla suffixes. We observe that thosesuffixes having high value of intercept are forming derived words whose base frequencies aresubstantially high as compared to their derived forms. Moreover, we also observe that highintercept value for a given suffix indicates higher inclination towards decomposition ratherthan whole word access.From the above analysis we observe that decomposition of a Bangla polymorphemicword not only depends upon the base and derived word frequencies, but also depends uponthe characteristics of the given suffix. That is, whether or not a polymorphemic word willbe accessed via decomposition or by whole word access depends on several factors like thefrequency distribution between the base word and the derived word, type and token frequency6 Similar results were reported for the English suffixes in Hay and Baayen (2001).123J Psycholinguist Resof the suffix, and the degree of affixation between the base word or the stem and the suffix.Thus, in spite of having both derived and stem frequency ratio and suffix type/token ratiofalls below the threshold frequency τ , a Bangla polymorphemic word may not show thedecomposition phenomena due to the fact the degree of affixation between the stem andthe suffix may be weak. Therefore, in the following sections we will explore the degree ofaffixation between the stem and the affix. Accordingly, we will first identify the role of suffixfrequencies (type and token) in determining the decomposition of Bangla polymorphemicwords.Model-4: Suffix Type/Token Ratio ModelThe type frequency is defined as the total number of distinct words associated with an affix.On the other hand, token frequency of a suffix is the total</s>
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<s>number of times a suffix is attachedwith a word. In this model the type token frequency ratio of individual suffixes was taken intoaccount to study the decomposition of Bangla polymorphemic words. As suggested earlier,lesser is the token frequency of a suffix greater is its chances in getting parsed in a wordattached with it. Type frequency of a suffix exhibits the potentiality of a suffix in forming anentirely new word. In other words, it is a count of how many different types of words a suffixcan derive from the base word. Taking the ratio between the type and the token frequencyof every suffix that can attach with a given stem, we determine the degree of affixation of agiven stem and a suffix. Through this information we try to predict the access mechanismsof Bangla polymorphemic words. We believe as the degree of affixation between a stem anda suffix decreases the higher is the probability of decomposition of the target derived word.Therefore, hypothesis for this model can be given as, for a given Bangla polymorphemicword if the type/token frequency ratio (in logarithmic scale) of a given suffix, attached to aword, exceeds a predefined threshold τ, then the word will be accessed as a whole otherwisethe derived word will be decomposed into the corresponding stem and suffix. The thresholdvalue for the surface and stem frequency ratio is computed by taking the average of the ratiobetween surface word and base word frequency of around 2,000 polymorphemic words. Weestimated the average and hence the threshold to be around 0.08. Therefore, the proposedmodel can be represented as:Decomposabili t y (w) =TRUE, i ff requency(T ype(Wsu f f i x))f requency(T oken(Wsu f f i x))≤ τFALSE, otherwiseSimilar to the previous models, our new model is evaluated over a set of 500 Bangla poly-morphemic words where the stem and the suffixes are transparent (i.e the suffix is fully orpartly recognizable). The performance of the model as presented in Table 11 shows 69 %accuracy.Although, model-4 does not throw any improvement over model-3 in terms of accuracy,we observed that model-4 performs best in determining the true negative values (see Table 11)and thus, can better predict those words which does not shows the decomposition phenomena.On the other hand, model-3 possesses a high precision of 70 % and can better detect the truepositive values (199) as compared to model-4. Therefore, despite of having same accuracy,both the model shows equal strength in classifying different types of word. This observationis further illustrated in Table 10 which depicts a list of words that were given as an input tomodel-3.From Table 10, we observe that words like, meghlA (CLOUDY), nibAsI (RESIDENT)and Alokita (SHINE) despite of having very week priming effects, are wrongly classified as123J Psycholinguist ResTable 10 List of sample prime target pairs given as an input to model-3 and model-4 and their performancePrime-targets Base/surfacefrequency ratioPriming type Model-3 result Model-4 resultjIbanta- jIba (lively–living) 0.47 0 1 0bA.DioYAlA- bA.Di (Housekeeper–House) 0.01 1 1 1bayaska- bayasa (Old–Age) 0.05 1 1 1nibAsI- nibAsa (Residence–Resident) 0.04 0 0 1meghalA- megha</s>
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<s>(Cloudy–Cloud) 0.02 0 0 1Alokita- Alo (Lightning–Light) 0.02 0 0 1rAShTrIYa- rAShTra (National–Nation) 2.05 1 0 0nAchunI- nAcha (Dancer–Dance) 0.05 0 0 0Priming type = 1 implies significant degree of priming is observed for the word pairs, and priming type = 0implies no priming or less priming is observed. For Model-3 and Model-4 Result, 1 implies the model correctlyclassifies the decomposition of the derived word and 0 implies failure to classify the word correctlyTable 11 Summarizing theresults of Type/Token ratio modelModel-4: type/token ratio(values out of 500 words)PerformanceFalse positive 100 Precision (%) 50True negative 158 Recall (%) 85True positive 100 F-Measure (%) 63False negative 15 Accuracy (%) 69decomposable words because of their low base and surface words frequency ratios (0.04, 0.02,and 0.02 respectively). On the other hand, when these words are provided as an input to model-4, they have been correctly classified as non-decomposable. This may be accounted due tothe fact that suffixes attached to these words have got low type/token ratios (0.01, 0.03, and0.018 respectively) and thus difficult to decompose. However, both the proposed models failsto explain the decomposition of word like, rAShtriYa (NATIONAL) and non-decompositionof word like nAchuni (DANCER) which needed a more deeper analysis. Nevertheless, theabove experimental data and our observation further strengthen our claim that only base andsurface word frequencies are not the only factors responsible for the decomposition factor andsuffix properties plays equally important role in determining the decomposition of Banglapolymorphemic words in the mental lexicon. Hence, we argue that combining the above twomodels can better predict the decomposability of Bangla polymorphemic words. But, beforethat we would further like to analyze whether along with the type/token ratio, the productivityof a suffix plays any role in morphological decomposition (Table 11).Model-5: Suffix Productivity in Morphological DecompositionIn this section our objective is to identify the degree of affixation of a given suffix and aword. In other word, we try to compute how well a given suffix can be attached with a givenstem. This is done by computing the productivity of a suffix. Although it has been proposedthat suffix type frequency can be a determiner of its productivity, yet it has been argued thatproductivity is multifaceted and can be assessed in different ways (Hay and Plag 2004). We,in this paper apply the same technique as proposed by Hay and Plag (2004) to compute theproductivity of Bangla suffixes. There are mainly three components of productivity, P, P*,123J Psycholinguist ResTable 12 Correlation between the suffix type frequency, token frequency, happex count and conditioneddegree of productivityType frequency Type frequency Type frequency Type frequencyType frequency – 0.97 0.91 −0.726Token frequency – – 0.909 −0.694Happex – – – −0.701Productivity – – – –and V. V is the “type frequency” of a suffix. That is, the number of different type of wordswith which the suffix is attached. P is the “conditioned degree of productivity” and is theprobability that we are encountering a word with a suffix(S) and it is representing a new type.The productivity of a suffix S (denoted as P(S)) is therefore computed as:Productivi t</s>
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<s>y (Si ) = P (W |Si ∩ f requency (w) = 1) = Hcount (S)= Number of happex wi th that su f f i xNumber of token containing the su f f i x(N )Where, Hcount is the number of hapaxes with the given affix S and NS is the number of tokenscontaining the suffix (N). Hapaxes are those words which occur exactly once in the corpus.Hapaxes and their counts are important in linguistics because they reveal how potential asuffix is in forming an entirely new word, what is its strength in producing new and rarewords.P* is the “hapaxed-conditioned degree of productivity”. It expresses the probability thatwhen an entirely new word is encountered it will contain the suffix. It is measured by calcu-lating all hapaxes in the corpus with that affix / total number of hapaxes in the corpus. Thus,P* is computed as:P∗ = P (Happex |Si ) = Number of happex in the cor pus wi th the su f f i x SiT otal Number of happex in the cor pusFinally, we add P and P* to get the productivity value of every suffix. We have chosen 27suffixes, 9 of them are very frequent (type frequency ranges from 1,000 to 1,700 words andtoken frequency 3,000–7,000), 9 are moderately frequent and the rest are least frequent (typefrequency below 100 and token frequency below 500). For every suffix, we have computedthe type and token frequencies, the number of hapex count and their productivity. We alsocomputed the correlation between the above factors (see Table 12).We found that, for Bangla, both type and token frequencies significantly correlates amongthemselves as well as with the happex count which again is inversely correlates to the produc-tivity of the suffix. This implies as the type/token frequency of a suffix increases the higherare the chances of the suffix to form happexes. Although a negative correlation is observedbetween type/token frequencies, happex count with the suffix productivity, however, no sig-nificant correlation could be drawn between them. Therefore, we aim to identify the role ofsuffix productivity in the processing of words in the mental lexicon. Accordingly, we com-puted both the conditioned degree of productivity (P) and hapaxed-conditioned degree ofproductivity (P*) and finally plotted a regression curve between them. The equation of theregression line is depicted in the equation below:P = 0.040 × P∗ − 0.124123J Psycholinguist ResTable 13 Summarizing theresults of suffix productivitymodelModel-5: suffixproductivity(values out of500 words)PerformanceFalse positive 44 Precision (%) 84True negative 129 Recall (%) 73True positive 240 F-Measure (%) 73False negative 87 Accuracy (%) 74We hypothesized that, any point lying above the regression line will be processed via decom-position otherwise they will be processed as a whole. We have evaluated our model with thesame set of 500 Bangla polymorphemic words that has been used for the priming experiments.Table 13 depicts the overall result of the evaluation. We observed that as the productivity ofthe suffix increases, the probability of decomposition of a word also get increases. For exam-ple, we observe that the suffixes “-wAlA”, ”-giri”, “-tba”, and</s>
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<s>“-panA” are highly productive(ranges between 0.6 and 0.9) as compared to the suffixes “-A”, “-Ani”, “-tama”, and “-I”.Therefore, words having productive suffixes will be more prone to decomposition than theless productive ones. We validate our model with the same 500 words that has been used tovalidate the previous models. We found an accuracy of around “74 %”.One important observation that can be made from Tables 11 and 13 is that, both the model4 and model 5 performs best in determining the true negative values. It is also observed thatModel-4 possess a high recall value of (85 %) but having a low precision of (50 %) on the otherhand results of Model-3 and model-5 possess a high precision of 70 and 84 % respectively.This implies, model-4 can accurately predict those words for which decomposition will nottake place. On the other hand model-3 and model-5 accurately identifies those words for whichdecomposition will occur. Thus, we argue that combining the above three models togethercan enhance the performance. Hence, in the next section we will present a new model thatcombines the power of the above three models in determining the decomposability of Banglapolymorphemic words.Model-6: Combining Model-3, Model-4, and Model-5From the discussions of the last section we combined model 3, 4 and 5 together to get anew enhanced model. The combination of the models were done by performing both logicalAND an logical OR operation on the outputs of Model-3, Model-4 and Model-5. We observethat performance of the OR operation results in a slightly improved accuracy, but both ofthem are comparable. Thus we have considered performing the logical OR operation overthe feature models. This is represented as:Decomposabili t y (w) =T RU E, i f (M3 (w)M4 (w)M5 (w) = 1F AL SE, otherwiseSimilar to the earlier models, we evaluate Model-6 with the same 500 words used in earliermodels. The results are depicted in Table 14 (column 7). A comparison of results of ourfinal proposed model with that of the existing ones is depicted in TableResultTable1. Theperformance of our final model shows an accuracy of 80 % with a precision of 87 % anda recall of 78 %. This outperforms the performance of the other models discussed earliersections. However, around 22 % of the test words that include words like, rAShTrIya, nAchuni,123J Psycholinguist ResTable 14 Summarizing the comparative results of the existing frequency based models and our proposedmodelsM1 BF M2 SF M3 LOG(SF) versusLOG (BF)TYP/TKNvsSF/BFM5 P, P*,V M6 COMBINEDFalse positive 135 111 88 133 44 32True negative 111 88 143 212 129 175True positive 199 155 199 133 240 228False negative 56 143 67 20 87 64Precision (%) 60 58 70 50 84 87Recall (%) 78 51 75 75 73 78F-Measure (%) 68 54 72 60 74 82Accuracy (%) 62 49 68 69 74 80M-1 to M-6 corresponds to Model-1 to Model-6. BF = Base frequency model, SF = Surface frequency model,TYP = Suffix type frequency, TKN = Suffix token frequency, Combine = Combining models 3, and 4 togethernishThAbAna, and juyADi, were wrongly classified by Model-5</s>
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<s>which the model fails tojustify. Thus, a more rigorous set of experiments and data analyses are required to predictaccess mechanisms of such Bangla polymorphemic words.General Discussion and ConclusionIn this paper we attempted to model the representation and processing of Bangla morpho-logically complex words. Our aim is to determine whether a Bangla polymorphemic wordis accessed as a whole or is it decomposed into its constituent morphemes and is recognizedaccordingly. We tried to answer this question through two different angles. First, we haveconducted a series of psycholinguistic experiments based on masked priming paradigm. Thereaction time of the subjects for recognizing various lexical items under appropriate condi-tioning reveals important facts about their organization in the brain which are discussed inthe paper.Our initial results show that morphologically related prime-target pairs do prime eachirrespective of their orthographic or semantic relatedness. On the other hand, prime-targetpairs that are morphologically opaque do not exhibit any priming effects even if they areorthographically or semantically related. Further, RT analysis of individual words showed thata significant number of Bangla polymorphemic words do not decompose during processing.These observations lead us to believe that mental representation and access of polymorphemicword in Bangla shows the partial decomposition model. We also observe that several otherfactors including word usage frequency, orthographic complexities, word length and spellingaffect the overall word recognition time and accuracy. Each of these factors call for rigorousexperimentation for understanding the exact nature of their inter dependencies.In the second approach, we tried developed a computational model that can predict therecognition process of Bangla polymorphemic words. In order to do so, we have exploredthe individual roles of different linguistic features of a Bangla morphologically complexword and accordingly proposed different feature models. We finally combine the indi-vidual feature models together and propose a new model that can accurately predict theprocessing of a Bangla morphologically complex word. The combination has been done by123J Psycholinguist Resperforming both logical OR and logical AND operation over the outputs of the individualfeature models. Performance of the logical OR operation is slightly better than that of theAND operation. Finally, we observed that, decomposition of Bangla morphologically com-plex words depends upon several factors like, the base and surface word frequency, suffixtype/token ratio, suffix family size and suffix productivity. The performance of the combinedmodel shows an accuracy of 80 % and this outperform the performance of the individualfeature models described in the paper. However, our proposed combined model (MODEL-6)fails to explain the processing phenomena of rest of the 20 % words for which further exper-iments and RT analysis are required. To the best of the knowledge of the authors there is noother work on computational modeling of Bangla polymorphemic words against which wecould benchmark our results.ReferencesAitchison, J. (2005). Words in the mind: An introduction to the mental lexicon. London: Taylor & Francis.Ambati, B., Dulam, G., Husain, S., & Indurkhya, B. (2009). Effect of jumbling the order of letters in a word onreading ability for indian languages: An eye-tracking study: Proceedings of the 31st Annual Conferenceof the Cognitive Science Society. Austin, TX: Cognitive Science</s>
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<s>masked-priming investigation of morphological representation. Journal of Experimental Psychology: Learning,Memory, and Cognition, 23(4), 829.Grainger, J., Colé, P., & Segui, J. (1991). Masked morphological priming in visual word recognition. Journalof Memory and Language, 30(3), 370–384.Hay, J. & Baayen, H.( 2001). Parsing and productivity. In Yearbook of morphology, p. 35.Hay, J., & Plag, I. (2004). What constrains possible suffix combinations? On the interaction of grammaticaland processing restrictions in derivational morphology. Natural Language & Linguistic Theory, 22(3),565–596.Jo, E. (2000). Crowding affects reading in peripheral vision. Intel Science Talent Search, 1–15.Marslen-Wilson, W., Bozic, M., & Randall, B. (2008). Early decomposition in visual word recognition:Dissociating morphology, form, and meaning. Language and Cognitive Processes, 23(3), 394–421.Marslen-Wilson, W., Tyler, L., et al. (1997). Dissociating types of mental computation. Nature, 387(6633),592–593.Marslen-Wilson, W., Tyler, L., Waksler, R., & Older, L. (1994). Morphology and meaning in the english mentallexicon. Psychological Review, 101(1), 3.Marslen-Wilson, W., & Zhou, X. (1999). Abstractness, allomorphy, and lexical architecture. Language andCognitive Processes, 14(4), 321–352.Milin, P., Kuperman, V., Kostic, A., & Baayen, R. (2009). Paradigms bit by bit: An information-theoreticapproach to the processing of paradigmatic structure in inflection and derivation. Analogy in Grammar:Form and Acquisition, pp. 214–252.Moscoso del Prado Martn, F., Deutsch, A., Frost, R., Schreuder, R., De Jong, N. H., et al. (2005). Changingplaces: A cross-language perspective on frequency and family size in dutch and hebrew. Journal of Memoryand Language, 53(4), 496–512.Pylkkänen, L., Feintuch, S., Hopkins, E., & Marantz, A. (2004). Neural correlates of the effects of morpho-logical family frequency and family size: An meg study. Cognition, 91(3), B35–B45.Rastle, K., Davis, M., Marslen-Wilson, W., & Tyler, L. (2000). Morphological and semantic effects in visualword recognition: A time-course study. Language and Cognitive Processes, 15(4–5), 507–537.Schreuder, R., & Baayen, R. (1997). How complex simplex words can be. Journal of Memory and Language,37, 118–139.Taft, M. (2004). Morphological decomposition and the reverse base frequency effect. Quarterly Journal ofExperimental Psychology Section A, 57(4), 745–765.Taft, M., & Forster, K. (1975). Lexical storage and retrieval of prefixed words. Journal of Verbal Learningand Verbal Behavior, 14(6), 638–647.123 Computational Modeling of Morphological Effects in Bangla Visual Word Recognition Abstract Introduction Psycholinguistic Study of Bangla Polymorphemic Words through Masked Priming Experiments Materials and Methods Procedure Participants Results Analysis of RTs for Lexical Items Analysis of High RT Lexical Items Analysis of Error Rates Discussion Applying Frequency Models to Bangla Polymorphemic Words Model-1: Base Word Frequency Effects Model-2: Derived Word Frequency Effect Discussion Model 3: Relative Frequency between Base and the Derived Words Exploring the Role of Suffixes in Processing of Bangla Words Model-4: Suffix Type/Token Ratio Model Model-5: Suffix Productivity in Morphological Decomposition Model-6: Combining Model-3, Model-4, and Model-5 General Discussion and Conclusion References</s>
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<s>Paper Title (use style: paper title)Bengali Word Embeddings and It's Application inSolving Document Classification ProblemAdnan AhmadResearcher, Search Engine PipilikaDepartment of Computer Science and EngineeringShahjalal University of Science and TechnologySylhet, Bangladesh.adnan.ahmad@student.sust.eduMohammad Ruhul AminPhD student, Computer Science DepartmentStony Brook UniversityNY 11790, USAmoamin@cs.stonybrook.edu Abstract—In this paper, we present Bengali word embeddingsand it’s application in the classification of news documents. Wordembeddings are multi-dimensional vectors that can be created byexploiting the linguistic context of the words in large corpus. Togenerate the embeddings, we collected Bengali news document oflast five years from the major daily newspapers. Wordembeddings are generated using the Neural Network basedlanguage processing model Word2vec. We use the vectorrepresentations of the Bengali words to cluster them using K-means algorithm. We show that those clusters can be useddirectly to perform various natural language processing task bysolving the problem of Bengali news document classification. Weuse the Support Vector Machine (SVM) for the classification taskand achieve ~91% F1-score. The accuracy of our methoddemonstrates that our word embeddings could capture thesemantics of word from the respective context correctly. Keywords— Bengali, Word Embedding, Word2vec, DocumentClassification, Word ClusterI. INTRODUCTION In the recent years, word embeddings or the vectorrepresentation of the words have been proved to achievesignificant performance in the language modeling and in thenatural language processing (NLP) tasks [1]. The wordembedding of a word represent the word in a multi-dimensionalspace in which the semantically similar words are placed closerto each other and non-related words are placed far from oneanother [2][3][4]. Thus, these distributed vector representationscan be used to learn the abstract relationship among the wordsby using unsupervised clustering methods. The features ofthose clusters can be used very effectively to solve variousNLP tasks like document classification, sentiment analysis,parts-of-speech tagging, named entity recognition and machinetranslation etc [1][4]. Bengali is a highly inflected as well as morphologically richlanguage [5]. A slight modification in a word can change it’sform to express a completely different meaning from theoriginal one in terms of tense, mood, person, number andgender to name a few [5]. So, clustering words that sharessimilar concepts in Bengali is a very challenging task. Very fewattempts are taken to cluster Bengali words. Those attempts aremainly based on the N-gram language model [6], whereclusters are generated by considering the words with theirfrequency in a context up to trigram. The N-gram model onlyconsider the consecutive words and their relative frequencies ina N-gram window. The probability of a word in the context iscalculated only from the context of previous words. Thisprobability cannot be used to represent the distance orsimilarity among all the words in a language. Thus, N-grammodel cannot be used directly for clustering the semanticallysimilar words together, let alone solving the other NLPproblems in Bengali. In this paper, we present the application of Begnali wordembeddings to solve document classification problem inBengali. We create vector representation of Bengali wordsusing Word2vec model [2]. We use t-SNE, an efficientdimension reduction technique to map those multi-dimensionalvectors into two-dimensional space [7]. We then apply K-means clustering to find the clusters of word embeddings,those are found in close proximity in the multi-dimensionalspace</s>
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<s>[8]. Finally, We use the cluster information of Bengaliword embeddings as features to solve the problem of Bengalinews document classification by using the machine learningalgorithm, support vector machine (SVM) [9]. Our modelachieve the accuracy of ~91% which justifies that the modelcan be be used successfully in solving many other NLPproblems in Bengali. Specifically, our contributions include:Largest Collection of Bengali word embeddings: We aregoing to release the largest collection of Bengali wordembeddings. To our knowledge, the only available wordembeddings for Bengali were published under the Polyglotproject from the Data Science Lab at State University of StonyBrook [4]. Polyglot used the Bengali contents in Wikipedia andcreated word embeddings for ~55,000 words. For our work, wecollected news contents of last five years from 13 majornewspapers and analyzed ~52,000,000 of lines to release wordembeddings for ~210,000 Bengali words.Document Classification without Preprocessing: Weshow that clustering information of Bengali words embeddingscan be used as a feature to solve Bengali documentclassification problem. Previously, it was considered thataccuracy of stemming and key word identification need to beimproved for preprocessing the document for better documentclassification. But, in this paper our method shows that we canuse the word embeddings directly for news documentclassification; hence, we show that document classification canbe done independently from the other preprocessing steps. II. BACKGROUND STUDYA. Bengali Word Embeddings Word-vectors or so-called distributed representationshave a long history by now, starting perhaps from work of S.Bengio et al [10] where he obtained word-vectors as by-product of training neural-net language model. A lot of relatedresearches demonstrated that these vectors do capturesemantic relationship between words [11]. Word2vec is apopular word embedding model which is created by using atwo layer Neural Network (NN) and skip-gram technique andsuccessfully used for many NLP tasks [1][2]. There are fewother popular word embedding models, namely, Polyglot,Glove and Gensim [3][4][12]. To the best of our knowledge,only Polyglot published the word embeddings for ~55,000Bengali words by from the Bengali wikipedia. Abhishek et al.created a neural lemmatizer using Bengali word embeddingsgenerated by Word2vec model [13] using a relatively smalldataset.B. Bengali Word Clustering A pioneer work on word clustering is proposed by Brownet al, where they used n-gram language model [14]. Brownclusters have been used successfully in a variety of NLPapplications [15]. Another attempt using n-gram model isreported by Korkmaz et al; they used a similarity function anda greedy algorithm to put the words into clusters [16]. Ding etal presented Naive Bayes method for English in classifyingwords using surrounding context words as features [17].Further, many other approaches have been reported inliterature for other languages like Russian, Arabic, Chineseand Japanese. As mentioned earlier, very few works has beendone on Bengali word clustering so far. Tanmoy et al proposedsemantic clustering of words using synset to identify Bengalimulti-word expressions [18]. Sabir et al proposed anunsupervised machine learning technique to develop Bengaliword clusters based on their semantic and contextual similarityusing N-gram language model [6]. C. Bengali Document classification For text classification in other languages, i.e. English,Chinese, Hindi, Arabic and European languages, variousnumber of supervised learning techniques has been used, suchas Association Rules</s>
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<s>[19], Neural Network [20], K-NearestNeighbour [21], Decision Tree [22], Naïve Bays [23], SupportVector Machine [24], and N-grams [25] etc. Previous workson document classification for Bengali are mainly based on N-gram [26], Naïve Bays [27], Stochastic Gradient Descentbased classifier [28]. The features of word clusters have beenused to perform various NLP tasks for a long time. We cameacross a work of Y. Yuan et al, who used word clusters createdfrom Word2vec to perform document clustering in Chineselanguage by applying Support Vector Machine (SVM) [29]. III. METHODOLOGYA. Neural Network and Word2vec Words occurring in the same or similar contexts tend toconvey similar meaning. There are many approaches tocomputing semantic similarity between words based on theirdistribution in a corpus. Word2vec models are shallow, two-layer neural networks which is trained in the unsupervisedfashion to reconstruct linguistic context of words. Word2vectakes a large corpus of text as input for training and produces aset of vectors called embeddings, typically of several hundreddimensions, with each unique word in the corpus. Givenenough data, usage and contexts, Word2vec can make highlyaccurate guesses about a word’s meaning based on pastappearances. Word2vec produces word embeddings in one oftwo ways: either using context to predict a target word, amethod known as continuous bag of words, or CBOW; or usinga word to predict a target context, which is called Skip-gram(Figure 1).Fig. 1. Two ways to compute Word2vec model: 1. Continuous Bag of Words(CBOW) and 2. Skip-gram.To generate Bengali word embeddings, we use the skip-grammethod because Skip-gram works well with small amount ofthe training data and can represent well even rare words orphrases [2]. In our work, we create a Bengali Word2vec modelwhich contains online newspaper articles from 13 differentnewspaper of year 2010-2015, a collection of 2185701documents. To our best knowledge, this is the largest Word2vecmodel for Bengali language. We create two separate Word2vecmodels of dimension size 100 and 200, using defaultparameters. Also, we learned the vector for unknown word(UNK). Later, we use those word vectors to create wordclusters for Bengali and use those clusters in documentclassification task. B. Dimensionality Reduction and Clustering As Word2vec model represents words as vector, we candirectly apply K-means clustering algorithm on top of it. Butapplying K-means and performing all the calculations in highdimensional feature space is time-consuming. So, beforeapplying K-means, we use dimension reduction techniquecalled t-distributed Stochastic Neighbor Embedding (t-SNE) toreduce the dimension size of the vectors into two [7]. It is anonlinear dimensionality reduction technique that isparticularly well-suited for embedding high-dimensional datainto a space of two or three dimensions, which can then bevisualized in a scatter plot. This benefits the process in twoways: first, it takes less time to compute the clusters using K-means; second, clusters can be plotted and visualized into a 2Dplane. K-means clustering is a method of vector quantization,that is popular for cluster analysis in data mining. The wholeprocess is represented in the Figure 2. Fig. 2. Clustering the word embeddings: 1. Create word embeddings for eachword in a corpus, 2. Reduce the vector dimension, 3. Create clusters of vectors representing words.C. Support Vector</s>
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<s>Machine (SVM) as Document classifier For Document classification task, we use Support VectorMachine (SVM) classification algorithm. SVM is a popularsupervised learning algorithm for classification task and manyresearcher attempted to perform document classification taskusing it [30]. Given a set of training documents, each documentmarked with a particular category, an SVM training algorithmbuilds a model that assigns new examples into one of thepredefined categories. Fig. 3. The process of training SVM classifier.Figure 3 shows the process of training SVM classifier usingword clusters as feature. Each row represents a document. Firstcolumn represents the category id. Rest of the columnsrepresents cluster id's and how many words of a particulardocument belong to a certain cluster. The model was trainedusing the default parameters.IV. EXPERIMENTSOur experiment is completed in three steps. Firstly, we stripoff the html tags for all the news crawled from Bengali onlinenewspapers. We use those data to train the Word2vec modeland generate embeddings. Secondly, we apply dimensionreduction technique and K-means clustering on subset ofwords to create clusters. Finally, using word clusters asfeatures, we perform document classification task to evaluatethe word embeddings and clusters.A. Data for Word2vec In general, Word2vec model takes a huge amount of data(typically about 100 billion words) as training text to createaccurate models; but there are not much Bengali data availableonline. We collected online newspaper articles from 13different Bengali newspapers of year 2010 to 2015. Totalnumber of article is 218,5701, totalling 51,920,010 sentences.Most of the sentences contain 5 to 25 words. For the Word2Vecmodel, we only took words which occurred at least 5 times inthe documents, totaling 210,535 words. Figure 3 shows thefrequencies of sentences with various sentence lengths. Fig. 4. Sentence length VS countB. Data for Document Classification To perform document clustering, we collected ~20,000Bengali online newspaper documents, each labeled into itsparticular class. We use 7 general classes like Sports,Entertainment, Politics etc. Overview of the data is given inTable I. We separate 70% document of each class fortraining and 30% document of each class for testing.TABLE I. TOTAL NUMBER OF DOCUMENTS FOR CLUSTERING Class Class name Number of documents0 Sports 22321 Entertainment 26552 Accident and Crime 41363 International 22504 Science & Tech. 29065 Politics 28086 Economics 2718C. Clustering Word Embeddings for Document Classification Using the training data mentioned above, we train ourWord2vec model. We use deeplearning4j1, a javaimplementation of Word2vec model, using default experimentsetup with the context window size 5 and min word frequency5. We created two different models with vector size of 100 and200 for our experiment. Vocabulary size of the final model is210,535. As words are represented as vectors in a Word2vecmodel, that makes each word independent of their contexts. Wecan take any two words and calculate distance or similaritybetween them. That means, we can use the whole corpus or anysubset of words from the corpus to cluster the words by directlyapplying K-means clustering algorithm. But before applying K-means to those word vectors, first we reduce the dimension sizeof the vectors to two, using t-SNE dimension reductiontechnique and then apply K-means. We use R implementationof both the t-SNE2</s>
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<s>and K-means3. We create severalexperiments for the different embeddings size and number ofclusters, using K={100, 200, 300, 400, 500, 600}. For eachdocument, we use the number of words in a particular clusteras features to perform SVM classification. We use Scikit-learnlibSVM4, a popular python implementation of SVM to performmulti class classification. We discuss the outcome of ourexperiments in the result sections. V. RESULTSIn order to evaluate the word clusters, three methods can beused, i.e. measuring the internal coherence of clusters,embedding the clusters in an application, or evaluating againsta manually generated answer key [31]. The first method isgenerally used by the clustering algorithms themselves. Thesecond method is especially relevant for applications that candeal with noisy clusters and avoids the need to generate answerkeys specific to the word clustering task. The third methodrequires a gold standard such as WordNet[32] or some otherontological resource. English and a number of other languageshave resources such as WordNet [33][34]. Unfortunately, there1 http://deeplearning4j.org/word2vec2 https://lvdmaaten.github.io/tsne/3 https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html4 http://scikit-learn.org/stable/modules/svm.htmlexists no WordNet for Bengali words. In order to evaluate theclusters, we perform a NLP task, which is Bengali DocumentClassification, by using the information of word clusters asfeatures and measured the accuracy of that task.In Figure 4, we show the graph of performance measure usingWord Clusters VS Accuracy, for both the embeddings size of100 and 200, and K={100, 200, 300, 400, 500, 600}. Weachieve our best result, ~91.02% F1-score using K= 600 forboth the embedding sizes of 100 and 200. As we reduce theembedding dimension into two before clustering, we observeno significant effect of the number of actual embeddingdimension on clustering as well as classification task. But, wemust also mention that for the embedding size less than 100,word embeddings did not result in meaningful clusters;meaning contextually unrelated words showed up in the samecluster which resulted in poor classification performance. Wealso observed such problem while using Polyglot for creatingBengali embeddings. Polyglot uses only 64 dimensions for theembeddings which failed to capture the contexts in Bengalilanguage. In Figure 5, we show that word clusters become moremeaningful and accurate when the number of K in K-means islarge. When we cluster the words with relatively small value ofK (K=50), the words of the clusters become generalized forwhich semantic and contextual similarity is hard to relate. Butwhen the value of K is large (K =600), we see more accurateand meaningful clusters are constructed. Fig. 5. Cluster size VS Classification accuracyIn our classification, the news can be classified into sevenclasses. For each of those classes, we measure the precision,recall and f1-score and show in Table II for the experimentalsetup of D=100 and K= 600. Our test data contains 4713documents, which is 30% of the total dataset and separatefrom the training set. Here, the result shows averageprecision of 91%, recall of 90% and F1-score of 91%. TABLE II. CLASSIFICATION REPORT (D = 100, K = 600)Class Precision Recall F1-score Test DocumentSports 0.98 0.94 0.96 528Entertainment 0.93 0.93 0.93 627Accident and Crime 0.92 0.91 0.91 996International 0.90 0.89 0.89 566Science & Tech. 0.91 0.86 0.88 677Politics 0.93 0.87 0.90 653Economics 0.77</s>
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<s>0.92 0.84 654avg / total 0.91 0.90 0.91 4713We evaluate our approach with the TREC evaluationtechnique to produce Precision-Recall graph [35]. In Figure6, we show the Precision Recall graph for the experimentmentioned above.Fig. 6. Overall Precision-Recall curve of Document Classification (D = 100,K = 600)In Figure 7, we present Confusion Matrix to elucidate theperformance of our classification model on test dataset. Thefigures show the confusion matrix with normalization by classsupport size.Fig. 7. Normalized Confusion Matrix (D = 100, K = 600)From Figure 7, we can see that, our classifier performedslightly poor for class 4 (International) and class 5 (Science andTech.). We observe that those classes are often confused withclass 6 (Economics). One possible reason for that is, Economiccategory documents often contains common words and topicsof both the Science & Tech. and International news. This is apossible reason for which our system achieved F1-score <95%. Another reason for which we think our system couldperform better is the size of the data for word embeddingtraining. The more data we use to train, the more accurate thevector representations will be, therefore the quality of clusters.Typically, corpus contains 100 billion words for language likeEnglish. Bengali has very few online content compared to that. VI. CONCLUSIONWe demonstrate that Bengali word embeddings can be usedto create word clusters that capture the semantic relationshipof words from the context. We use the clusteringinformation of words as features to perform NLP task likedocument classification. We achieve the performance of~91% as F1-score. We show that we can achieve such aperformance without any preprocessing of the Bengali text.It proves the effectiveness of the word embedding model forperforming the NLP tasks in Bengali. We observed that thelarger the text corpus we use, the better the word clusterscan be formed; so we will collect more Bengali data togenerate the embeddings. We will continue our study tounderstand how can we learn the vector representation foreach word better by studying other existing embeddingmodels: Polyglot, Gensim and Glove. We will also study theeffect of dimension reduction on the documentclassification. We will use our understanding from thosestudy to solve other classification problem like POS tagging,NER and Sentiment Analysis in Bengali.VII. ACKNOWLEDGEMENTThis research was partially supported by Search EnginePipilika, which is a Bengali search engine initiallydeveloped by Shahjalal University of Science & Technology(SUST). We thank Pipilika team, specially Mahbubur RubTalha and Tushar Chakraborty, they provided us newspaperdata that greatly assisted the research.REFERENCES[1] Collobert, Ronan, et al. "Natural language processing (almost) fromscratch." Journal of Machine Learning Research 12.Aug (2011): 2493-2537.[2] Mikolov, T., and J. Dean. "Distributed representations of words andphrases and their compositionality." Advances in neural informationprocessing systems (2013).[3] Pennington, Jeffrey, Richard Socher, and Christopher D. Manning."Glove: Global Vectors for Word Representation." EMNLP. Vol. 14. 2014.[4] Al-Rfou, Rami, Bryan Perozzi, and Steven Skiena. "Polyglot:Distributed word representations for multilingual nlp." arXiv preprintarXiv:1307.1662 (2013).[5] Ali, Md Nawab Yousuf, et al. "Morphological analysis of bangla wordsfor universal networking language." Digital Information Management, 2008.ICDIM 2008. Third International Conference on. IEEE, 2008.[6] Ismail, Sabir, and M. Shahidur Rahman. "Bangla word clustering basedon N-gram language</s>
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