| { |
| "paper_id": "W97-0126", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T04:34:26.698874Z" |
| }, |
| "title": "A Statistical Approach to Thai Morphological Analyzer*", |
| "authors": [ |
| { |
| "first": "Kawtrakul", |
| "middle": [], |
| "last": "Asanee", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "Natural Language Processing and Intelligent Information System Technology Research Laboratory", |
| "institution": "Kasetsart University", |
| "location": { |
| "country": "THAILAND" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Thumkanon", |
| "middle": [], |
| "last": "Chalathip", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "Natural Language Processing and Intelligent Information System Technology Research Laboratory", |
| "institution": "Kasetsart University", |
| "location": { |
| "country": "THAILAND" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Three nontrivial problems of Thai morphological processing are word boundary ambiguity, tagging ambiguity and implicit spelling errors. These problems cause a lot of difficulty to the parser due to the alternative or erroneous chain of word. This work attempts to provide a computational solution, called Word Filtering, to those linguistic phenomena. The filtering process calculates the probabilities of all possible chains of tagged words using a Markov Model. The most likely sequence of tagged word is the one that maximizes the chain probabilities. However, it may be an erroneous chain which has an implicit spelling error. Therefore, the Word Filtering, also, includes the scanning process that detect and correct these errors. Both filtering and scanning process use a statistical data infonuation collected ~om the hand-ta.~ed corpus. The experiment has shown that word filtering can eliminate most of the alternative word sequences. Moreover: this tcelmique is fairly good at the implicit error correction.", |
| "pdf_parse": { |
| "paper_id": "W97-0126", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Three nontrivial problems of Thai morphological processing are word boundary ambiguity, tagging ambiguity and implicit spelling errors. These problems cause a lot of difficulty to the parser due to the alternative or erroneous chain of word. This work attempts to provide a computational solution, called Word Filtering, to those linguistic phenomena. The filtering process calculates the probabilities of all possible chains of tagged words using a Markov Model. The most likely sequence of tagged word is the one that maximizes the chain probabilities. However, it may be an erroneous chain which has an implicit spelling error. Therefore, the Word Filtering, also, includes the scanning process that detect and correct these errors. Both filtering and scanning process use a statistical data infonuation collected ~om the hand-ta.~ed corpus. The experiment has shown that word filtering can eliminate most of the alternative word sequences. Moreover: this tcelmique is fairly good at the implicit error correction.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "One of the major problcrns in many languages, such as Japanese, Chinese, Korean and Thai, is word boundary ambiguity because these languages do not have any delimiters between words. The second problem is tagging ambigui~' which occurs when there is more than one tag for one word. Another probleau is implicit spelling error that occurs because some incorrect words can be found in a diotionm3, .This problem is very hard to solve with a simple approach, such as dictionary approach. Thai morphological ~n~lysis must face these three problems which cause many possible alternative or/and the erroneous chains of words. These problems generate a lot of unuecessary work for the parser. In order to simplify the parser and speed it up, three important points to bear in mind when cousidering the morphological processing are neat segmentation of characters into words, part of speech tagging selection, and implicit spelling error detection. This work attempts to provide a computational solution, called Word Filtering, to handle those three points prior to parsing.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1." |
| }, |
| { |
| "text": "The proposed model of Tb.ai morphological analysis consists of three steps: sentence segmenting, spell checking and word ill, ring. Using word fonnation rules and a dictionary look up algorithm in the first step, all possible word groups with all possible tags will be given. If there is any explicit error, the second step, that is spell checking, will give a suggestion about a set of most likely words. However, the implicit spelling error may still exist and will affect the parser. That is, the parser must search a large set of tagged word combinations in order to choose the fight one. Thus, the main goal of word filtering is to reduce the combination of unuseful tagged words and to identify implicit spelling error. The proposed Word Filtering method consists of two steps: a filtering process and a scanning process. The first process will try to filter out any incorrect word boundary and any unsuitable tag. The second process detects and corrects the implicit spelling error by generating the new words for the detected error.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1." |
| }, |
| { |
| "text": "The basic idea of the filtering process is to calculate the probabilities of all possible chains of tagged words by using a trigram of the Markov Model. The most likely sequences of tagged words are the ones that maximize chain probabilities. Nevertheless, they may be an erroneous chain which have implicit speRing errors. Thus, the Word Filtering, also includes the scanning process to detect and correct the error. At this step, a set of words will be generated by a generating function and be replaced to the detected word. The most likely sequences of correct words arc the ones that maximize chain probabilities. Both filtering and scanning processes use the statistical infomaation collected from the hand-tagged corpus. From results of the experiments on small corpus (about 10,000 sentences), word filter can criminate alternative word sequences and can correct the implicit error quite well.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1." |
| }, |
| { |
| "text": "In the following section, key problems in Thai morphological analysis are described. Then, we present the overview of a computational morphological processing in section 3. In the section 4, the concept of how to use the statistical information to handle word boundary ambiguity, tagging ambiguity and implicit spelling error will be explained. Finally-, we present the conclusion result of*.he experiment.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1." |
| }, |
| { |
| "text": "There are three nontrivial problems of Thai morphological processing: word boundary ambiguity, tagging ambiguity and impficit spelling errors.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Key Problems in Thai Morphological Analysis", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Thai seutences are simile to the Japanese's and Chinese's in terms of having no blank space to mark each words within the same sentence. Additionally most of Thai words are multisyllabic words. Some of them contains more than monosyllabic words as parts of its component. This causes word boundary ambiguity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "Let C be a sequence of characters: C = c~ c.2 c3 ... Let W be a sequence of words: W = wl w2 ... w, where wl = cn..~ Giving a stream of characters, the possible word segmentation is as following : As shown above, the word in \"C,CzC~C4Cs\" pattern has two ambiguous forras. One is \"C, C2\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "stream of characters wl w2 ~.__l--~c,c, I Ic, c,c., I [c,c,c,c,c, t---'4c,c,c~ . I Ic, c, I L.._l----.~c~c~ I Ic, c, I {clc:Gc, J l , '--~ClC, C,C, I (1.1) (].2)", |
| "eq_num": "(2." |
| } |
| ], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "and \"C~C4Cs\". The other one is \"'C,C:C~\" and \"'C4Cs\". In our corpus, more than 50% of sentences include word boundary ambiguity-. The assignment of part of speech to the segmented word is also effected by the word boundary ambiguity. This causes the ambiguous pattern in a sentence The example is as shown in the following:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "E~mple 1 ABCD ABCD ? ? The ambiguous patterns of the above sentence are :", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "~D a) N (The boat) b) N (The boat) c) N (The boat) d) N (The boat) N (ox) N (ox) B~ V (go down) V (go down) V (shake) V (shake) conj (because) conj (because) conj (because) conj (because) ~t3 N (ox) N (ox) B~ V (go down) V (shake) V (go down) V (shake) ~B N (the boat) N (the boat) N (the boat) N (the boat)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "In the above example, only c) and d) are the meaningful sentences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Boundary Ambiguity", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "A Thai word can have more than one part of speech. This tag ambiguity can cause a large set of tagged word combinations. Consider the following example\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tag Ambiguity", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "[Ex~ple 2", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tag Ambiguity", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "1B'11t 1~1,.~'1 ~ Iqll N |J \u2022 \u00b0 (which), (which), (at), (place), l 4) ch 5) el: i (di.~) (dish) i (4 lags) (4 tags) ~~ [) relpron: i~ (which), \u2022 D v: Dprep: (eat) (at),", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tag Ambiguity", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "(place),", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Dcn:", |
| "sec_num": null |
| }, |
| { |
| "text": "(2 tags] (2 tags) (4 \"tags)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "N", |
| "sec_num": null |
| }, |
| { |
| "text": "The above multiple-tagged words give 1024 combinations of word chain. However, only one word chain is correct. Figure 1 . shows tag ambiguity in our corpus. As we can see, there are about 95% of the words are ambiguous with regards to the t.~s they take.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 111, |
| "end": 119, |
| "text": "Figure 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "N", |
| "sec_num": null |
| }, |
| { |
| "text": "Number Both word boundary and tag ambiguity increase the complexity in syntax analysis. It also increases the amount of time used for parsing the sentences. Besides these two ambiguities, spelling errors in Thai, called implicit spelling errors, also cause a lot more work for the parser.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Number ofta~s", |
| "sec_num": null |
| }, |
| { |
| "text": "Implicit spelling errors, one of ill-formedness usually encountered in documents, are caused by either carelessness or lack of knowledge. This type of error can not be d~ectecl by simply using a dictionary approach. There are three kinds of typing errors caused by the carelessness: Missing, Keyboard Mistyping, and Swapping as the examples in the following :", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implicit Spelling Error", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "Cause Type Missing Mistyping Swapping carelessness lack of knowledge (t)his ~ his free ~ fee fa(t) -'~ far both \"9' boat (n)o \"> on form -'-Y from", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implicit Spelling Error", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "In case of lacking of knowledge, the errors occured from the unclear speech confuse to the typist. Additionally, they also occurs from the confusion in writing since there are many forms in one sound. See the following example.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implicit Spelling Error", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "c,q c.,c, c,c.,qc, ~') (prep) (V) I push raft down water until leg twist (I push the raft to the river and twisted my leg.) The implicit spelling errors can occur much easier in Thai than in English and Japanese (in Hiragana) because the errors ah~ays involve using aword that has a similar pronunciation. There are about 20-30% of Thai words that can cause this kind of the confusion to typist. Additionally, there are 2 characters in one key pad (see figure 2) . Thus, keyboard mistyping increases the way of implicit misspelling which can not be dete~ed easily using the dictionary -based approach.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 453, |
| "end": 462, |
| "text": "figure 2)", |
| "ref_id": "FIGREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Example 3", |
| "sec_num": null |
| }, |
| { |
| "text": "a) pron V N prep N conj. N N prep b) pron V N prep N conj. N V a) pron V V N conj. N N prep a) pron V V", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "1.The ambiguous patterns caused by word boundary ambiguity are :", |
| "sec_num": null |
| }, |
| { |
| "text": "1} In this work, we attempt to provide a computational solution to handle these three nontrivial problems for making ~ejob of a parser much easier. The next section will present the overview of the system. Input sentence is a stream of characters without explicit delimiters. Using word formation rules and Lexicon base look up algorithm [KAW95(a)], the word segmenting process, wig provide all possible word grouping with all possible tags. If there is any explicit error then the second step, the spell checking process, will be called to give a suggestion with a set of most likely, word [KAW9fi(a)]. However, an implicit spelliug error may stiU exist. In order to choose the right tagged word combination, word \u2022tering process will use the statistical association among words, coUected as a statistical base, to eliminate the alternative and/or erroneous chain of words which is caused by word boundary and tagging ambiguities and implicit spelling error. This paper concentrates only on the word filtering process. The detail of the process will be discussed next.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I o, ,,I m , I", |
| "sec_num": null |
| }, |
| { |
| "text": "A11 of word boundary, part of speech tag and implicit spelling error can be disambiguated by using a trigram model [CHAR 93] to calculate the probabilities of word cluster. The sentences shown in example 1, lc) and ld) are meaningful sentences. In other words, they have the strength of association of word in a chain more than la) and ld) have. The association between words in \"'the boat shake\" is stronger than in \"'the boat ox\". In example 2, we can also can find the most likely sequence of parts of speech by considering the previous part of speech. Since an implicit spelling error affects both meaning and tag, (such as : ~ (fly) : v ~ ~J~ (on): preposition) the special process is needed .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Filtering", |
| "sec_num": "4." |
| }, |
| { |
| "text": "Consequently, word filtering will consists of two processes : a filtering process used to eliminate unuseful ragged word combinations and a scanning process used to detect and correct an implicit spelling error by generating a new set of words according to the cause of errors and selecting the one that maximizes the probabilities of word cluster. Both processes need to look up a statistical information collected from the hand-tagged corpus.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word Filtering", |
| "sec_num": "4." |
| }, |
| { |
| "text": "The ~alning corpus is a set of sentences, divided into two groups. Each sentence in the first group is prepared to give a context for a word which has a possibility to become an implicit spelling error, and a context for a sequence of words that have word boundary ambiguity. The second group are sentences prepared to give a context for a multiple-tagged word. All of these sentences have already segmented and tagged. A statistical information will be collected as a statistical base to support both filtering process and SCanning process. Thus, collected statistics not only emphasize on the frequency of using individual words but also on the cluster of words.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The Training Corpus", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "A Trigram Model [CHAR 93] is utilized to calculate the probabilities of word cluster, i.e. how the previous two words affects the probabilities of next word. This can be explain in equation 1 In order to estimate the probability of J~w, lw,_~,,_ , )in (1), the following equation is used:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Trigram Model", |
| "sec_num": "4.2.1" |
| }, |
| { |
| "text": "where PeO0 is the estimated probability for Xbased on some count C: So to estimate the probability of w, appear after \"w;..,,w,.l\", we count how many times the pair \"~,..,,w,.t\" appears in our corpus and how many times \"w,.2,w,4,w,\" appears and divide.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Trigram Model", |
| "sec_num": "4.2.1" |
| }, |
| { |
| "text": "Because of the sparse-data problem in trigram model, rather than equation 1 Thus, we can compute the better probabildes although the relevant trigram or bigram data are missing. The result from the experiment shows that the assigned values. I, 3, .6 to 2 1,/i,_,, ~ s, [CHAR 93] respectively, will give the satisfied solution for Thai word sequence probability. Using equation 3, the strength of association of words in a chain can be calculated.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Trigram Model", |
| "sec_num": "4.2.1" |
| }, |
| { |
| "text": "In order to handle the tagging ambiguity problem. A U'igram part of speech model is also used [DeRose 88] = P (,,Iw,) , (,,I,,_;,,_,) (,> Since the proposed model is provided for disambiguating both word boundary and tag, we use the average of probabilities calculated by the equation 3and (4) as the strength of a chain of tagged words and select the higher one as the most likely sequence of corrected word with their tags. For example, the strength of word chain (see the example l) in lc) higher than la) while the probabilities of the sequence of parts of speech of la) and Ic) are equal. Based on the average of the strength of word chain and the most likely sequence of parts of speech, Ic) will be selected as the solution of word segmentation and tagging. The first part of word filtering, i.e., the faltering process, calculates the strength of each tagged word combination. The combination(s) that gives the highest value will be the most likely sequence(s) of tagged words. In the second part, scanning process, an implicit spelling error will be detected and corrected [KAW95(b)]. That is, the weakest strength of word cluster will be assumed to have an implicit spelling error. Then a new set of words which are generated according to the causes of error will be replaced to flint detected word one by one. A replaced word which gives the highest value of the strength of word chain will be a solution of an implicit spelling error.", |
| "cite_spans": [ |
| { |
| "start": 110, |
| "end": 117, |
| "text": "(,,Iw,)", |
| "ref_id": null |
| }, |
| { |
| "start": 120, |
| "end": 133, |
| "text": "(,,I,,_;,,_,)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Trigram Model", |
| "sec_num": "4.2.1" |
| }, |
| { |
| "text": "From the results of the experiment shown below, word filter can eliminate many of alternative word sequences and corr~t the unplicit error. This result makes the job of the parser much easier and speeds it up. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusion", |
| "sec_num": "5." |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "The work reported in this paper was supported by the National Research Council of Thailand. Thanks are also due to Patcharee Varasai, Supapas Kurntanode, Thitipom Tharapoome and Mukda \u2022 Suktarajam for their helpful on the preparation of the training corpus, Puchong Uthayopat and Amarin Deemagam for their helpful to complete this paper..", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgment", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
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| "num": null, |
| "urls": [], |
| "raw_text": "Kawtrakul Asanee, Muangymman Parinee, Maneekanjanajing Nopparat, \"'A Morphological Analyzer for Writing Production Assistant System\", A Progress Report to the National Research Council of Thailand, 1995. Kawtrakul Asanee, \"'A statistical Approach to Ambiguity Filtering in WPA System\", A Progress Report to the National Research Council of Thailand, 1995. Shiho Nobesawa, \"Segmenting a Sentence into Morphemes Using Statistic Information bet~ een Words\", COLING94, 1994, pp.227-233.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "num": null, |
| "type_str": "figure", |
| "text": "A Statistical Approach to Thai Morphological Analyzer is a part of WPA (Writing Production Assistant) Project supported by the National Research Council of Thailand.", |
| "uris": null |
| }, |
| "FIGREF1": { |
| "num": null, |
| "type_str": "figure", |
| "text": "t", |
| "uris": null |
| }, |
| "FIGREF2": { |
| "num": null, |
| "type_str": "figure", |
| "text": "Two level key pads for Thai character.", |
| "uris": null |
| }, |
| "FIGREF3": { |
| "num": null, |
| "type_str": "figure", |
| "text": "computalional model consists of word segmenting, spelling checking and word filtering processes is proposed to handle the morphological problems mentioned earlier. (see figure 3An overview of Thai Morphological Analysis.", |
| "uris": null |
| }, |
| "FIGREF4": { |
| "num": null, |
| "type_str": "figure", |
| "text": "There are two parts in word filtering (see thefigure below)", |
| "uris": null |
| }, |
| "FIGREF5": { |
| "num": null, |
| "type_str": "figure", |
| "text": "Two Parts of Word Filtering", |
| "uris": null |
| } |
| } |
| } |
| } |
