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Lexicon-Grammar tables are a very rich syntactic lexicon for the French language. This linguistic database is nevertheless not directly suitable for use by computer programs, as it is incomplete and lacks consistency. Tables are defined on the basis of features which are not explicitly recorded in the lexicon. These features are only described in literature. Our aim is to define for each tables these essential properties to make them usable in various Natural Language Processing (NLP) applications, such as parsing.
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Constructions définitoires des tables du Lexique-Grammaire
| 1,400
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Categorial type logics, pioneered by Lambek, seek a proof-theoretic understanding of natural language syntax by identifying categories with formulas and derivations with proofs. We typically observe an intuitionistic bias: a structural configuration of hypotheses (a constituent) derives a single conclusion (the category assigned to it). Acting upon suggestions of Grishin to dualize the logical vocabulary, Moortgat proposed the Lambek-Grishin calculus (LG) with the aim of restoring symmetry between hypotheses and conclusions. We develop a theory of labeled modal tableaux for LG, inspired by the interpretation of its connectives as binary modal operators in the relational semantics of Kurtonina and Moortgat. As a linguistic application of our method, we show that grammars based on LG are context-free through use of an interpolation lemma. This result complements that of Melissen, who proved that LG augmented by mixed associativity and -commutativity was exceeds LTAG in expressive power.
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Tableaux for the Lambek-Grishin calculus
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This paper describes a probabilistic top-down parser for minimalist grammars. Top-down parsers have the great advantage of having a certain predictive power during the parsing, which takes place in a left-to-right reading of the sentence. Such parsers have already been well-implemented and studied in the case of Context-Free Grammars, which are already top-down, but these are difficult to adapt to Minimalist Grammars, which generate sentences bottom-up. I propose here a way of rewriting Minimalist Grammars as Linear Context-Free Rewriting Systems, allowing to easily create a top-down parser. This rewriting allows also to put a probabilistic field on these grammars, which can be used to accelerate the parser. Finally, I propose a method of refining the probabilistic field by using algorithms used in data compression.
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A probabilistic top-down parser for minimalist grammars
| 1,402
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In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COCS) is based on k-means linear clustering of the sentences. Experimental results obtained using COCS algorithm are presented.
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Learning Taxonomy for Text Segmentation by Formal Concept Analysis
| 1,403
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It is usual to consider that standards generate mixed feelings among scientists. They are often seen as not really reflecting the state of the art in a given domain and a hindrance to scientific creativity. Still, scientists should theoretically be at the best place to bring their expertise into standard developments, being even more neutral on issues that may typically be related to competing industrial interests. Even if it could be thought of as even more complex to think about developping standards in the humanities, we will show how this can be made feasible through the experience gained both within the Text Encoding Initiative consortium and the International Organisation for Standardisation. By taking the specific case of lexical resources, we will try to show how this brings about new ideas for designing future research infrastructures in the human and social sciences.
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Stabilizing knowledge through standards - A perspective for the
humanities
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We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.
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A PDTB-Styled End-to-End Discourse Parser
| 1,405
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A temporal analysis of emoticon use in Swedish, Italian, German and English asynchronous electronic communication is reported. Emoticons are classified as positive, negative and neutral. Postings to newsgroups over a 66 week period are considered. The aggregate analysis of emoticon use in newsgroups for science and politics tend on the whole to be consistent over the entire time period. Where possible, events that coincide with divergences from trends in language-subject pairs are noted. Political discourse in Italian over the period shows marked use of negative emoticons, and in Swedish, positive emoticons.
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Emoticonsciousness
| 1,406
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This article describes a method to build syntactical dependencies starting from the phrase structure parsing process. The goal is to obtain all the information needed for a detailled semantical analysis. Interaction Grammars are used for parsing; the saturation of polarities which is the core of this formalism can be mapped to dependency relation. Formally, graph patterns are used to express the set of constraints which control dependency creations.
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Motifs de graphe pour le calcul de dépendances syntaxiques complètes
| 1,407
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"What other people think" has always been an important piece of information during various decision-making processes. Today people frequently make their opinions available via the Internet, and as a result, the Web has become an excellent source for gathering consumer opinions. There are now numerous Web resources containing such opinions, e.g., product reviews forums, discussion groups, and Blogs. But, due to the large amount of information and the wide range of sources, it is essentially impossible for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer or seller of a product to accurately monitor customer opinions. For this reason, mining customer reviews, or opinion mining, has become an important issue for research in Web information extraction. One of the important topics in this research area is the identification of opinion polarity. The opinion polarity of a review is usually expressed with values 'positive', 'negative' or 'neutral'. We propose a technique for identifying polarity of reviews by identifying the polarity of the adjectives that appear in them. Our evaluation shows the technique can provide accuracy in the area of 73%, which is well above the 58%-64% provided by naive Bayesian classifiers.
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Opinion Polarity Identification through Adjectives
| 1,408
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Our study applies statistical methods to French and Italian corpora to examine the phenomenon of multi-word term reduction in specialty languages. There are two kinds of reduction: anaphoric and lexical. We show that anaphoric reduction depends on the discourse type (vulgarization, pedagogical, specialized) but is independent of both domain and language; that lexical reduction depends on domain and is more frequent in technical, rapidly evolving domains; and that anaphoric reductions tend to follow full terms rather than precede them. We define the notion of the anaphoric tree of the term and study its properties. Concerning lexical reduction, we attempt to prove statistically that there is a notion of term lifecycle, where the full form is progressively replaced by a lexical reduction. ----- Nous \'etudions par des m\'ethodes statistiques sur des corpus fran\c{c}ais et italiens, le ph\'enom\`ene de r\'eduction des termes complexes dans les langues de sp\'ecialit\'e. Il existe deux types de r\'eductions : anaphorique et lexicale. Nous montrons que la r\'eduction anaphorique d\'epend du type de discours (de vulgarisation, p\'edagogique, sp\'ecialis\'e) mais ne d\'epend ni du domaine, ni de la langue, alors que la r\'eduction lexicale d\'epend du domaine et est plus fr\'equente dans les domaines techniques \`a \'evolution rapide. D'autre part, nous montrons que la r\'eduction anaphorique a tendance \`a suivre la forme pleine du terme, nous d\'efinissons une notion d'arbre anaphorique de terme et nous \'etudions ses propri\'et\'es. Concernant la r\'eduction lexicale, nous tentons de d\'emontrer statistiquement qu'il existe une notion de cycle de vie de terme, o\`u la forme pleine est progressivement remplac\'ee par une r\'eduction lexicale.
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La réduction de termes complexes dans les langues de spécialité
| 1,409
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This document presents Annotated English, a system of diacritical symbols which turns English pronunciation into a precise and unambiguous process. The annotations are defined and located in such a way that the original English text is not altered (not even a letter), thus allowing for a consistent reading and learning of the English language with and without annotations. The annotations are based on a set of general rules that make the frequency of annotations not dramatically high. This makes the reader easily associate annotations with exceptions, and makes it possible to shape, internalise and consolidate some rules for the English language which otherwise are weakened by the enormous amount of exceptions in English pronunciation. The advantages of this annotation system are manifold. Any existing text can be annotated without a significant increase in size. This means that we can get an annotated version of any document or book with the same number of pages and fontsize. Since no letter is affected, the text can be perfectly read by a person who does not know the annotation rules, since annotations can be simply ignored. The annotations are based on a set of rules which can be progressively learned and recognised, even in cases where the reader has no access or time to read the rules. This means that a reader can understand most of the annotations after reading a few pages of Annotated English, and can take advantage from that knowledge for any other annotated document she may read in the future.
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Annotated English
| 1,410
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We reformulate minimalist grammars as partial functions on term algebras for strings and trees. Using filler/role bindings and tensor product representations, we construct homomorphisms for these data structures into geometric vector spaces. We prove that the structure-building functions as well as simple processors for minimalist languages can be realized by piecewise linear operators in representation space. We also propose harmony, i.e. the distance of an intermediate processing step from the final well-formed state in representation space, as a measure of processing complexity. Finally, we illustrate our findings by means of two particular arithmetic and fractal representations.
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Geometric representations for minimalist grammars
| 1,411
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Arabic morphological analysis is one of the essential stages in Arabic Natural Language Processing. In this paper we present an approach for Arabic morphological analysis. This approach is based on Arabic morphological automaton (AMAUT). The proposed technique uses a morphological database realized using XMODEL language. Arabic morphology represents a special type of morphological systems because it is based on the concept of scheme to represent Arabic words. We use this concept to develop the Arabic morphological automata. The proposed approach has development standardization aspect. It can be exploited by NLP applications such as syntactic and semantic analysis, information retrieval, machine translation and orthographical correction. The proposed approach is compared with Xerox Arabic Analyzer and Smrz Arabic Analyzer.
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Developing a New Approach for Arabic Morphological Analysis and
Generation
| 1,412
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Grishin proposed enriching the Lambek calculus with multiplicative disjunction (par) and coresiduals. Applications to linguistics were discussed by Moortgat, who spoke of the Lambek-Grishin calculus (LG). In this paper, we adapt Girard's polarity-sensitive double negation embedding for classical logic to extract a compositional Montagovian semantics from a display calculus for focused proof search in LG. We seize the opportunity to illustrate our approach alongside an analysis of extraction, providing linguistic motivation for linear distributivity of tensor over par, thus answering a question of Kurtonina&Moortgat. We conclude by comparing our proposal to the continuation semantics of Bernardi&Moortgat, corresponding to call-by- name and call-by-value evaluation strategies.
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Polarized Montagovian Semantics for the Lambek-Grishin calculus
| 1,413
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We address the problem of inferring a speaker's level of certainty based on prosodic information in the speech signal, which has application in speech-based dialogue systems. We show that using phrase-level prosodic features centered around the phrases causing uncertainty, in addition to utterance-level prosodic features, improves our model's level of certainty classification. In addition, our models can be used to predict which phrase a person is uncertain about. These results rely on a novel method for eliciting utterances of varying levels of certainty that allows us to compare the utility of contextually-based feature sets. We elicit level of certainty ratings from both the speakers themselves and a panel of listeners, finding that there is often a mismatch between speakers' internal states and their perceived states, and highlighting the importance of this distinction.
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Recognizing Uncertainty in Speech
| 1,414
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The limited range in its abscissa of ranked letter frequency distributions causes multiple functions to fit the observed distribution reasonably well. In order to critically compare various functions, we apply the statistical model selections on ten functions, using the texts of U.S. and Mexican presidential speeches in the last 1-2 centuries. Dispite minor switching of ranking order of certain letters during the temporal evolution for both datasets, the letter usage is generally stable. The best fitting function, judged by either least-square-error or by AIC/BIC model selection, is the Cocho/Beta function. We also use a novel method to discover clusters of letters by their observed-over-expected frequency ratios.
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Fitting Ranked English and Spanish Letter Frequency Distribution in U.S.
and Mexican Presidential Speeches
| 1,415
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Existing grammar frameworks do not work out particularly well for controlled natural languages (CNL), especially if they are to be used in predictive editors. I introduce in this paper a new grammar notation, called Codeco, which is designed specifically for CNLs and predictive editors. Two different parsers have been implemented and a large subset of Attempto Controlled English (ACE) has been represented in Codeco. The results show that Codeco is practical, adequate and efficient.
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Codeco: A Grammar Notation for Controlled Natural Language in Predictive
Editors
| 1,416
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This article presents a fragment of a new comparative dictionary "A comparative dictionary of names of expansive action in Russian and Bulgarian languages". Main features of the new web-based comparative dictionary are placed, the principles of its formation are shown, primary links between the word-matches are classified. The principal difference between translation dictionaries and the model of double comparison is also shown. The classification scheme of the pages is proposed. New concepts and keywords have been introduced. The real prototype of the dictionary with a few key pages is published. The broad debate about the possibility of this prototype to become a version of Russian-Bulgarian comparative dictionary of a new generation is available.
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Materials to the Russian-Bulgarian Comparative Dictionary "EAD"
| 1,417
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To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via two experiments, including one that reports competitive accuracies for unsupervised grammar induction without gold standard part-of-speech tags.
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A Universal Part-of-Speech Tagset
| 1,418
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Chinese characters can be compared to a molecular structure: a character is analogous to a molecule, radicals are like atoms, calligraphic strokes correspond to elementary particles, and when characters form compounds, they are like molecular structures. In chemistry the conjunction of all of these structural levels produces what we perceive as matter. In language, the conjunction of strokes, radicals, characters, and compounds produces meaning. But when does meaning arise? We all know that radicals are, in some sense, the basic semantic components of Chinese script, but what about strokes? Considering the fact that many characters are made by adding individual strokes to (combinations of) radicals, we can legitimately ask the question whether strokes carry meaning, or not. In this talk I will present my project of extending traditional NLP techniques to radicals and strokes, aiming to obtain a deeper understanding of the way ideographic languages model the world.
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Seeking Meaning in a Space Made out of Strokes, Radicals, Characters and
Compounds
| 1,419
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This paper introduces the performance evaluation of statistical approaches for TextIndependent speaker recognition system using source feature. Linear prediction LP residual is used as a representation of excitation information in speech. The speaker-specific information in the excitation of voiced speech is captured using statistical approaches such as Gaussian Mixture Models GMMs and Hidden Markov Models HMMs. The decrease in the error during training and recognizing speakers during testing phase close to 100 percent accuracy demonstrates that the excitation component of speech contains speaker-specific information and is indeed being effectively captured by continuous Ergodic HMM than GMM. The performance of the speaker recognition system is evaluated on GMM and 2 state ergodic HMM with different mixture components and test speech duration. We demonstrate the speaker recognition studies on TIMIT database for both GMM and Ergodic HMM.
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Performance Evaluation of Statistical Approaches for Text Independent
Speaker Recognition Using Source Feature
| 1,420
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This article overviews the current state of the English-Lithuanian-English machine translation system. The first part of the article describes the problems that system poses today and what actions will be taken to solve them in the future. The second part of the article tackles the main issue of the translation process. Article briefly overviews the word sense disambiguation for MT technique using Google.
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English-Lithuanian-English Machine Translation lexicon and engine:
current state and future work
| 1,421
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The paper presents the design and development of English-Lithuanian-English dictionarylexicon tool and lexicon database management system for MT. The system is oriented to support two main requirements: to be open to the user and to describe much more attributes of speech parts as a regular dictionary that are required for the MT. Programming language Java and database management system MySql is used to implement the designing tool and lexicon database respectively. This solution allows easily deploying this system in the Internet. The system is able to run on various OS such as: Windows, Linux, Mac and other OS where Java Virtual Machine is supported. Since the modern lexicon database managing system is used, it is not a problem accessing the same database for several users.
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Multilingual lexicon design tool and database management system for MT
| 1,422
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In natural speech, the speaker does not pause between words, yet a human listener somehow perceives this continuous stream of phonemes as a series of distinct words. The detection of boundaries between spoken words is an instance of a general capability of the human neocortex to remember and to recognize recurring sequences. This paper describes a computer algorithm that is designed to solve the problem of locating word boundaries in blocks of English text from which the spaces have been removed. This problem avoids the complexities of speech processing but requires similar capabilities for detecting recurring sequences. The algorithm relies entirely on statistical relationships between letters in the input stream to infer the locations of word boundaries. A Viterbi trellis is used to simultaneously evaluate a set of hypothetical segmentations of a block of adjacent words. This technique improves accuracy but incurs a small latency between the arrival of letters in the input stream and the sending of words to the output stream. The source code for a C++ version of this algorithm is presented in an appendix.
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A statistical learning algorithm for word segmentation
| 1,423
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This paper presents an algorithm for identifying noun-phrase antecedents of pronouns and adjectival anaphors in Spanish dialogues. We believe that anaphora resolution requires numerous sources of information in order to find the correct antecedent of the anaphor. These sources can be of different kinds, e.g., linguistic information, discourse/dialogue structure information, or topic information. For this reason, our algorithm uses various different kinds of information (hybrid information). The algorithm is based on linguistic constraints and preferences and uses an anaphoric accessibility space within which the algorithm finds the noun phrase. We present some experiments related to this algorithm and this space using a corpus of 204 dialogues. The algorithm is implemented in Prolog. According to this study, 95.9% of antecedents were located in the proposed space, a precision of 81.3% was obtained for pronominal anaphora resolution, and 81.5% for adjectival anaphora.
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Computational Approach to Anaphora Resolution in Spanish Dialogues
| 1,424
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Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct.
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Acquiring Correct Knowledge for Natural Language Generation
| 1,425
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This article studies the emergence of ambiguity in communication through the concept of logical irreversibility and within the framework of Shannon's information theory. This leads us to a precise and general expression of the intuition behind Zipf's vocabulary balance in terms of a symmetry equation between the complexities of the coding and the decoding processes that imposes an unavoidable amount of logical uncertainty in natural communication. Accordingly, the emergence of irreversible computations is required if the complexities of the coding and the decoding processes are balanced in a symmetric scenario, which means that the emergence of ambiguous codes is a necessary condition for natural communication to succeed.
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On the origin of ambiguity in efficient communication
| 1,426
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These notes are a continuation of topics covered by V. Selegej in his article "Electronic Dictionaries and Computational lexicography". How can an electronic dictionary have as its object the description of closely related languages? Obviously, such a question allows multiple answers.
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Notes on Electronic Lexicography
| 1,427
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Model-based language specification has applications in the implementation of language processors, the design of domain-specific languages, model-driven software development, data integration, text mining, natural language processing, and corpus-based induction of models. Model-based language specification decouples language design from language processing and, unlike traditional grammar-driven approaches, which constrain language designers to specific kinds of grammars, it needs general parser generators able to deal with ambiguities. In this paper, we propose Fence, an efficient bottom-up parsing algorithm with lexical and syntactic ambiguity support that enables the use of model-based language specification in practice.
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Fence - An Efficient Parser with Ambiguity Support for Model-Driven
Language Specification
| 1,428
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We describe a new semantic relatedness measure combining the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. Our measure achieves the currently highest results on the WS-353 test: a Spearman rho coefficient of 0.79 (vs. 0.75 in (Gabrilovich and Markovitch, 2007)) when applying the measure directly, and a value of 0.87 (vs. 0.78 in (Agirre et al., 2009)) when using the prediction of a polynomial SVM classifier trained on our measure. In the appendix we discuss the adaptation of ESA to 2011 Wikipedia data, as well as various unsuccessful attempts to enhance ESA by filtering at word, sentence, and section level.
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A Semantic Relatedness Measure Based on Combined Encyclopedic,
Ontological and Collocational Knowledge
| 1,429
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Diacritical marks play a crucial role in meeting the criteria of usability of typographic text, such as: homogeneity, clarity and legibility. To change the diacritic of a letter in a word could completely change its semantic. The situation is very complicated with multilingual text. Indeed, the problem of design becomes more difficult by the presence of diacritics that come from various scripts; they are used for different purposes, and are controlled by various typographic rules. It is quite challenging to adapt rules from one script to another. This paper aims to study the placement and sizing of diacritical marks in Arabic script, with a comparison with the Latin's case. The Arabic script is cursive and runs from right-to-left; its criteria and rules are quite distinct from those of the Latin script. In the beginning, we compare the difficulty of processing diacritics in both scripts. After, we will study the limits of Latin resolution strategies when applied to Arabic. At the end, we propose an approach to resolve the problem for positioning and resizing diacritics. This strategy includes creating an Arabic font, designed in OpenType format, along with suitable justification in TEX.
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Design of Arabic Diacritical Marks
| 1,430
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The interest in text to speech synthesis increased in the world .text to speech have been developed formany popular languages such as English, Spanish and French and many researches and developmentshave been applied to those languages. Persian on the other hand, has been given little attentioncompared to other languages of similar importance and the research in Persian is still in its infancy.Persian language possess many difficulty and exceptions that increase complexity of text to speechsystems. For example: short vowels is absent in written text or existence of homograph words. in thispaper we propose a new method for persian text to phonetic that base on pronunciations by analogy inwords, semantic relations and grammatical rules for finding proper phonetic. Keywords:PbA, text to speech, Persian language, FPbA
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Use Pronunciation by Analogy for text to speech system in Persian
language
| 1,431
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We propose NEMO, a system for extracting organization names in the affiliation and normalizing them to a canonical organization name. Our parsing process involves multi-layered rule matching with multiple dictionaries. The system achieves more than 98% f-score in extracting organization names. Our process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. A high precision was also observed in normalization. NEMO is the missing link in associating each biomedical paper and its authors to an organization name in its canonical form and the Geopolitical location of the organization. This research could potentially help in analyzing large social networks of organizations for landscaping a particular topic, improving performance of author disambiguation, adding weak links in the co-author network of authors, augmenting NLM's MARS system for correcting errors in OCR output of affiliation field, and automatically indexing the PubMed citations with the normalized organization name and country. Our system is available as a graphical user interface available for download along with this paper.
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NEMO: Extraction and normalization of organization names from PubMed
affiliation strings
| 1,432
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BioSimplify is an open source tool written in Java that introduces and facilitates the use of a novel model for sentence simplification tuned for automatic discourse analysis and information extraction (as opposed to sentence simplification for improving human readability). The model is based on a "shot-gun" approach that produces many different (simpler) versions of the original sentence by combining variants of its constituent elements. This tool is optimized for processing biomedical scientific literature such as the abstracts indexed in PubMed. We tested our tool on its impact to the task of PPI extraction and it improved the f-score of the PPI tool by around 7%, with an improvement in recall of around 20%. The BioSimplify tool and test corpus can be downloaded from https://biosimplify.sourceforge.net.
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BioSimplify: an open source sentence simplification engine to improve
recall in automatic biomedical information extraction
| 1,433
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Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction. The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction. This approach renders all the advantages offered in other semi-supervised machine learning systems, and, unlike other proposed semi-supervised approaches, it can be used on top of different basic frameworks and algorithms. http://gradworks.umi.com/34/49/3449837.html
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An Effective Approach to Biomedical Information Extraction with Limited
Training Data
| 1,434
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In this paper we consider the problem of efficient computation of cross-moments of a vector random variable represented by a stochastic context-free grammar. Two types of cross-moments are discussed. The sample space for the first one is the set of all derivations of the context-free grammar, and the sample space for the second one is the set of all derivations which generate a string belonging to the language of the grammar. In the past, this problem was widely studied, but mainly for the cross-moments of scalar variables and up to the second order. This paper presents new algorithms for computing the cross-moments of an arbitrary order, and the previously developed ones are derived as special cases.
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Cross-moments computation for stochastic context-free grammars
| 1,435
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This paper introduces, an XML format developed to serialise the object model defined by the ISO Syntactic Annotation Framework SynAF. Based on widespread best practices we adapt a popular XML format for syntactic annotation, TigerXML, with additional features to support a variety of syntactic phenomena including constituent and dependency structures, binding, and different node types such as compounds or empty elements. We also define interfaces to other formats and standards including the Morpho-syntactic Annotation Framework MAF and the ISOCat Data Category Registry. Finally a case study of the German Treebank TueBa-D/Z is presented, showcasing the handling of constituent structures, topological fields and coreference annotation in tandem.
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Serialising the ISO SynAF Syntactic Object Model
| 1,436
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The usefulness of annotated corpora is greatly increased if there is an associated tool that can allow various kinds of operations to be performed in a simple way. Different kinds of annotation frameworks and many query languages for them have been proposed, including some to deal with multiple layers of annotation. We present here an easy to learn query language for a particular kind of annotation framework based on 'threaded trees', which are somewhere between the complete order of a tree and the anarchy of a graph. Through 'typed' threads, they can allow multiple levels of annotation in the same document. Our language has a simple, intuitive and concise syntax and high expressive power. It allows not only to search for complicated patterns with short queries but also allows data manipulation and specification of arbitrary return values. Many of the commonly used tasks that otherwise require writing programs, can be performed with one or more queries. We compare the language with some others and try to evaluate it.
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A Concise Query Language with Search and Transform Operations for
Corpora with Multiple Levels of Annotation
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We present a system to translate natural language sentences to formulas in a formal or a knowledge representation language. Our system uses two inverse lambda-calculus operators and using them can take as input the semantic representation of some words, phrases and sentences and from that derive the semantic representation of other words and phrases. Our inverse lambda operator works on many formal languages including first order logic, database query languages and answer set programming. Our system uses a syntactic combinatorial categorial parser to parse natural language sentences and also to construct the semantic meaning of the sentences as directed by their parsing. The same parser is used for both. In addition to the inverse lambda-calculus operators, our system uses a notion of generalization to learn semantic representation of words from the semantic representation of other words that are of the same category. Together with this, we use an existing statistical learning approach to assign weights to deal with multiple meanings of words. Our system produces improved results on standard corpora on natural language interfaces for robot command and control and database queries.
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Using Inverse lambda and Generalization to Translate English to Formal
Languages
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For a system to understand natural language, it needs to be able to take natural language text and answer questions given in natural language with respect to that text; it also needs to be able to follow instructions given in natural language. To achieve this, a system must be able to process natural language and be able to capture the knowledge within that text. Thus it needs to be able to translate natural language text into a formal language. We discuss our approach to do this, where the translation is achieved by composing the meaning of words in a sentence. Our initial approach uses an inverse lambda method that we developed (and other methods) to learn meaning of words from meaning of sentences and an initial lexicon. We then present an improved method where the initial lexicon is also learned by analyzing the training sentence and meaning pairs. We evaluate our methods and compare them with other existing methods on a corpora of database querying and robot command and control.
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Language understanding as a step towards human level intelligence -
automatizing the construction of the initial dictionary from example
sentences
| 1,439
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This paper investigates the efficiency of the EWC semantic relatedness measure in an ad-hoc retrieval task. This measure combines the Wikipedia-based Explicit Semantic Analysis measure, the WordNet path measure and the mixed collocation index. In the experiments, the open source search engine Terrier was utilised as a tool to index and retrieve data. The proposed technique was tested on the NTCIR data collection. The experiments demonstrated promising results.
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Query Expansion: Term Selection using the EWC Semantic Relatedness
Measure
| 1,440
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Stabler proposes an implementation of the Chomskyan Minimalist Program, Chomsky 95 with Minimalist Grammars - MG, Stabler 97. This framework inherits a long linguistic tradition. But the semantic calculus is more easily added if one uses the Curry-Howard isomorphism. Minimalist Categorial Grammars - MCG, based on an extension of the Lambek calculus, the mixed logic, were introduced to provide a theoretically-motivated syntax-semantics interface, Amblard 07. In this article, we give full definitions of MG with algebraic tree descriptions and of MCG, and take the first steps towards giving a proof of inclusion of their generated languages.
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Minimalist Grammars and Minimalist Categorial Grammars, definitions
toward inclusion of generated languages
| 1,441
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Formality is one of the most important dimensions of writing style variation. In this study we conducted an inter-rater reliability experiment for assessing sentence formality on a five-point Likert scale, and obtained good agreement results as well as different rating distributions for different sentence categories. We also performed a difficulty analysis to identify the bottlenecks of our rating procedure. Our main objective is to design an automatic scoring mechanism for sentence-level formality, and this study is important for that purpose.
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Inter-rater Agreement on Sentence Formality
| 1,442
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This paper presents a method to understand spoken Tunisian dialect based on lexical semantic. This method takes into account the specificity of the Tunisian dialect which has no linguistic processing tools. This method is ontology-based which allows exploiting the ontological concepts for semantic annotation and ontological relations for speech interpretation. This combination increases the rate of comprehension and limits the dependence on linguistic resources. This paper also details the process of building the ontology used for annotation and interpretation of Tunisian dialect in the context of speech understanding in dialogue systems for restricted domain.
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Building Ontologies to Understand Spoken Tunisian Dialect
| 1,443
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We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
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LexRank: Graph-based Lexical Centrality as Salience in Text
Summarization
| 1,444
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In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations.
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Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods
| 1,445
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A fundamental requirement of any task-oriented dialogue system is the ability to generate object descriptions that refer to objects in the task domain. The subproblem of content selection for object descriptions in task-oriented dialogue has been the focus of much previous work and a large number of models have been proposed. In this paper, we use the annotated COCONUT corpus of task-oriented design dialogues to develop feature sets based on Dale and Reiters (1995) incremental model, Brennan and Clarks (1996) conceptual pact model, and Jordans (2000b) intentional influences model, and use these feature sets in a machine learning experiment to automatically learn a model of content selection for object descriptions. Since Dale and Reiters model requires a representation of discourse structure, the corpus annotations are used to derive a representation based on Grosz and Sidners (1986) theory of the intentional structure of discourse, as well as two very simple representations of discourse structure based purely on recency. We then apply the rule-induction program RIPPER to train and test the content selection component of an object description generator on a set of 393 object descriptions from the corpus. To our knowledge, this is the first reported experiment of a trainable content selection component for object description generation in dialogue. Three separate content selection models that are based on the three theoretical models, all independently achieve accuracies significantly above the majority class baseline (17%) on unseen test data, with the intentional influences model (42.4%) performing significantly better than either the incremental model (30.4%) or the conceptual pact model (28.9%). But the best performing models combine all the feature sets, achieving accuracies near 60%. Surprisingly, a simple recency-based representation of discourse structure does as well as one based on intentional structure. To our knowledge, this is also the first empirical comparison of a representation of Grosz and Sidners model of discourse structure with a simpler model for any generation task.
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Learning Content Selection Rules for Generating Object Descriptions in
Dialogue
| 1,446
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The relationship between written and spoken words is convoluted in languages with a deep orthography such as English and therefore it is difficult to devise explicit rules for generating the pronunciations for unseen words. Pronunciation by analogy (PbA) is a data-driven method of constructing pronunciations for novel words from concatenated segments of known words and their pronunciations. PbA performs relatively well with English and outperforms several other proposed methods. However, the best published word accuracy of 65.5% (for the 20,000 word NETtalk corpus) suggests there is much room for improvement in it. Previous PbA algorithms have used several different scoring strategies such as the product of the frequencies of the component pronunciations of the segments, or the number of different segmentations that yield the same pronunciation, and different combinations of these methods, to evaluate the candidate pronunciations. In this article, we instead propose to use a probabilistically justified scoring rule. We show that this principled approach alone yields better accuracy (66.21% for the NETtalk corpus) than any previously published PbA algorithm. Furthermore, combined with certain ad hoc modifications motivated by earlier algorithms, the performance climbs up to 66.6%, and further improvements are possible by combining this method with other methods.
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A Probabilistic Approach to Pronunciation by Analogy
| 1,447
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Since 2006 we have undertaken to describe the differences between 17th century English and contemporary English thanks to NLP software. Studying a corpus spanning the whole century (tales of English travellers in the Ottoman Empire in the 17th century, Mary Astell's essay A Serious Proposal to the Ladies and other literary texts) has enabled us to highlight various lexical, morphological or grammatical singularities. Thanks to the NooJ linguistic platform, we created dictionaries indexing the lexical variants and their transcription in CE. The latter is often the result of the validation of forms recognized dynamically by morphological graphs. We also built syntactical graphs aimed at transcribing certain archaic forms in contemporary English. Our previous research implied a succession of elementary steps alternating textual analysis and result validation. We managed to provide examples of transcriptions, but we have not created a global tool for automatic transcription. Therefore we need to focus on the results we have obtained so far, study the conditions for creating such a tool, and analyze possible difficulties. In this paper, we will be discussing the technical and linguistic aspects we have not yet covered in our previous work. We are using the results of previous research and proposing a transcription method for words or sequences identified as archaic.
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Automatic transcription of 17th century English text in Contemporary
English with NooJ: Method and Evaluation
| 1,448
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A new approach to the problem of natural language understanding is proposed. The knowledge domain under consideration is the social behavior of people. English sentences are translated into set of predicates of a semantic database, which describe persons, occupations, organizations, projects, actions, events, messages, machines, things, animals, location and time of actions, relations between objects, thoughts, cause-and-effect relations, abstract objects. There is a knowledge base containing the description of semantics of objects (functions and structure), actions (motives and causes), and operations.
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Object-oriented semantics of English in natural language understanding
system
| 1,449
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Traditional language processing tools constrain language designers to specific kinds of grammars. In contrast, model-based language specification decouples language design from language processing. As a consequence, model-based language specification tools need general parsers able to parse unrestricted context-free grammars. As languages specified following this approach may be ambiguous, parsers must deal with ambiguities. Model-based language specification also allows the definition of associativity, precedence, and custom constraints. Therefore parsers generated by model-driven language specification tools need to enforce constraints. In this paper, we propose Fence, an efficient bottom-up chart parser with lexical and syntactic ambiguity support that allows the specification of constraints and, therefore, enables the use of model-based language specification in practice.
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A Constraint-Satisfaction Parser for Context-Free Grammars
| 1,450
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The goal of the present chapter is to explore the possibility of providing the research (but also the industrial) community that commonly uses spoken corpora with a stable portfolio of well-documented standardised formats that allow a high re-use rate of annotated spoken resources and, as a consequence, better interoperability across tools used to produce or exploit such resources.
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Data formats for phonological corpora
| 1,451
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In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.
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NP Animacy Identification for Anaphora Resolution
| 1,452
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This paper presents a novel algorithm to compute sentiment orientation of Chinese sentiment word. The algorithm uses ideograms which are a distinguishing feature of Chinese language. The proposed algorithm can be applied to any sentiment classification scheme. To compute a word's sentiment orientation using the proposed algorithm, only the word itself and a precomputed character ontology is required, rather than a corpus. The influence of three parameters over the algorithm performance is analyzed and verified by experiment. Experiment also shows that proposed algorithm achieves an F Measure of 85.02% outperforming existing ideogram based algorithm.
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Ideogram Based Chinese Sentiment Word Orientation Computation
| 1,453
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One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations.
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Individual and Domain Adaptation in Sentence Planning for Dialogue
| 1,454
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This paper presents the preliminary works to put online a French oral corpus and its transcription. This corpus is the Socio-Linguistic Survey in Orleans, realized in 1968. First, we numerized the corpus, then we handwritten transcribed it with the Transcriber software adding different tags about speakers, time, noise, etc. Each document (audio file and XML file of the transcription) was described by a set of metadata stored in an XML format to allow an easy consultation. Second, we added different levels of annotations, recognition of named entities and annotation of personal information about speakers. This two annotation tasks used the CasSys system of transducer cascades. We used and modified a first cascade to recognize named entities. Then we built a second cascade to annote the designating entities, i.e. information about the speaker. These second cascade parsed the named entity annotated corpus. The objective is to locate information about the speaker and, also, what kind of information can designate him/her. These two cascades was evaluated with precision and recall measures.
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ESLO: from transcription to speakers' personal information annotation
| 1,455
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In this paper, we evaluate various French lexica with the parser FRMG: the Lefff, LGLex, the lexicon built from the tables of the French Lexicon-Grammar, the lexicon DICOVALENCE and a new version of the verbal entries of the Lefff, obtained by merging with DICOVALENCE and partial manual validation. For this, all these lexica have been converted to the format of the Lefff, Alexina format. The evaluation was made on the part of the EASy corpus used in the first evaluation campaign Passage.
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Évaluation de lexiques syntaxiques par leur intégartion dans
l'analyseur syntaxiques FRMG
| 1,456
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In this paper, we summerize the work done on the resources of Modern Greek on the Lexicon-Grammar of verbs. We detail the definitional features of each table, and all changes made to the names of features to make them consistent. Through the development of the table of classes, including all the features, we have considered the conversion of tables in a syntactic lexicon: LGLex. The lexicon, in plain text format or XML, is generated by the LGExtract tool (Constant & Tolone, 2010). This format is directly usable in applications of Natural Language Processing (NLP).
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Construction du lexique LGLex à partir des tables du Lexique-Grammaire
des verbes du grec moderne
| 1,457
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This paper presents a work on extending the adverbial entries of LGLex: a NLP oriented syntactic resource for French. Adverbs were extracted from the Lexicon-Grammar tables of both simple adverbs ending in -ment '-ly' (Molinier and Levrier, 2000) and compound adverbs (Gross, 1986; 1990). This work relies on the exploitation of fine-grained linguistic information provided in existing resources. Various features are encoded in both LG tables and they haven't been exploited yet. They describe the relations of deleting, permuting, intensifying and paraphrasing that associate, on the one hand, the simple and compound adverbs and, on the other hand, different types of compound adverbs. The resulting syntactic resource is manually evaluated and freely available under the LGPL-LR license.
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Extending the adverbial coverage of a NLP oriented resource for French
| 1,458
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Algorithms of question answering in a computer system oriented on input and logical processing of text information are presented. A knowledge domain under consideration is social behavior of a person. A database of the system includes an internal representation of natural language sentences and supplemental information. The answer {\it Yes} or {\it No} is formed for a general question. A special question containing an interrogative word or group of interrogative words permits to find a subject, object, place, time, cause, purpose and way of action or event. Answer generation is based on identification algorithms of persons, organizations, machines, things, places, and times. Proposed algorithms of question answering can be realized in information systems closely connected with text processing (criminology, operation of business, medicine, document systems).
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Question Answering in a Natural Language Understanding System Based on
Object-Oriented Semantics
| 1,459
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A tagger is a mandatory segment of most text scrutiny systems, as it consigned a s yntax class (e.g., noun, verb, adjective, and adverb) to every word in a sentence. In this paper, we present a simple part of speech tagger for homoeopathy clinical language. This paper reports about the anticipated part of speech tagger for homoeopathy clinical language. It exploit standard pattern for evaluating sentences, untagged clinical corpus of 20085 words is used, from which we had selected 125 sentences (2322 tokens). The problem of tagging in natural language processing is to find a way to tag every word in a text as a meticulous part of speech. The basic idea is to apply a set of rules on clinical sentences and on each word, Accuracy is the leading factor in evaluating any POS tagger so the accuracy of proposed tagger is also conversed.
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Rule based Part of speech Tagger for Homoeopathy Clinical realm
| 1,460
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Twitter messages often contain so-called hashtags to denote keywords related to them. Using a dataset of 29 million messages, I explore relations among these hashtags with respect to co-occurrences. Furthermore, I present an attempt to classify hashtags into five intuitive classes, using a machine-learning approach. The overall outcome is an interactive Web application to explore Twitter hashtags.
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Exploring Twitter Hashtags
| 1,461
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This works aims to design a statistical machine translation from English text to American Sign Language (ASL). The system is based on Moses tool with some modifications and the results are synthesized through a 3D avatar for interpretation. First, we translate the input text to gloss, a written form of ASL. Second, we pass the output to the WebSign Plug-in to play the sign. Contributions of this work are the use of a new couple of language English/ASL and an improvement of statistical machine translation based on string matching thanks to Jaro-distance.
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Statistical Sign Language Machine Translation: from English written text
to American Sign Language Gloss
| 1,462
|
In this paper we describe function tagging using Transformation Based Learning (TBL) for Myanmar that is a method of extensions to the previous statistics-based function tagger. Contextual and lexical rules (developed using TBL) were critical in achieving good results. First, we describe a method for expressing lexical relations in function tagging that statistical function tagging are currently unable to express. Function tagging is the preprocessing step to show grammatical relations of the sentences. Then we use the context free grammar technique to clarify the grammatical relations in Myanmar sentences or to output the parse trees. The grammatical relations are the functional structure of a language. They rely very much on the function tag of the tokens. We augment the grammatical relations of Myanmar sentences with transformation-based learning of function tagging.
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Grammatical Relations of Myanmar Sentences Augmented by
Transformation-Based Learning of Function Tagging
| 1,463
|
Short Message Service (SMS) messages are largely sent directly from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data has not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors' concerns. Our live project collects new SMS message submissions, checks their quality and adds the valid messages, releasing the resultant corpus as XML and as SQL dumps, along with corpus statistics, every month. We opportunistically collect as much metadata about the messages and their sender as possible, so as to enable different types of analyses. To date, we have collected about 60,000 messages, focusing on English and Mandarin Chinese.
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Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus
| 1,464
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A step-to-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). We distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered a generated as an architecture by the network of relations in the network space; words are then not only related, but also positioned. These positions are expected rather than observed and therefore one can communicate meaning. Knowledge can be generated when these meanings can recursively be communicated and therefore also further codified.
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Visualization and Analysis of Frames in Collections of Messages: Content
Analysis and the Measurement of Meaning
| 1,465
|
Grishin's generalization of Lambek's Syntactic Calculus combines a non-commutative multiplicative conjunction and its residuals (product, left and right division) with a dual family: multiplicative disjunction, right and left difference. Interaction between these two families takes the form of linear distributivity principles. We study proof nets for the Lambek-Grishin calculus and the correspondence between these nets and unfocused and focused versions of its sequent calculus.
|
Proof nets for the Lambek-Grishin calculus
| 1,466
|
A formal theory based on a binary operator of directional associative relation is constructed in the article and an understanding of an associative normal form of image constructions is introduced. A model of a commutative semigroup, which provides a presentation of a sentence as three components of an interrogative linguistic image construction, is considered.
|
Formalization of semantic network of image constructions in electronic
content
| 1,467
|
We describe a Context Free Grammar (CFG) for Bangla language and hence we propose a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The proposed parser is a predictive parser and we construct the parse table for recognizing Bangla grammar. Using the parse table we recognize syntactical mistakes of Bangla sentences when there is no entry for a terminal in the parse table. If a natural language can be successfully parsed then grammar checking from this language becomes possible. The proposed scheme is based on Top down parsing method and we have avoided the left recursion of the CFG using the idea of left factoring.
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Recognizing Bangla Grammar using Predictive Parser
| 1,468
|
[This is the translation of paper "Arborification de Wikip\'edia et analyse s\'emantique explicite stratifi\'ee" submitted to TALN 2012.] We present an extension of the Explicit Semantic Analysis method by Gabrilovich and Markovitch. Using their semantic relatedness measure, we weight the Wikipedia categories graph. Then, we extract a minimal spanning tree, using Chu-Liu & Edmonds' algorithm. We define a notion of stratified tfidf where the stratas, for a given Wikipedia page and a given term, are the classical tfidf and categorical tfidfs of the term in the ancestor categories of the page (ancestors in the sense of the minimal spanning tree). Our method is based on this stratified tfidf, which adds extra weight to terms that "survive" when climbing up the category tree. We evaluate our method by a text classification on the WikiNews corpus: it increases precision by 18%. Finally, we provide hints for future research
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Wikipedia Arborification and Stratified Explicit Semantic Analysis
| 1,469
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Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database can not give a result. The following classes of problems are considered: a check of hypotheses for persons and non-typical actions, the determination of persons and circumstances for non-typical actions, planning actions, the determination of event cause and state of persons. To form an answer both deduction and plausible reasoning are used. As a knowledge domain under consideration is social behavior of persons, plausible reasoning is based on laws of social psychology. Proposed algorithms of inference and plausible reasoning can be realized in computer systems closely connected with text processing (criminology, operation of business, medicine, document systems).
|
Inference and Plausible Reasoning in a Natural Language Understanding
System Based on Object-Oriented Semantics
| 1,470
|
Here we describe work on learning the subcategories of verbs in a morphologically rich language using only minimal linguistic resources. Our goal is to learn verb subcategorizations for Quechua, an under-resourced morphologically rich language, from an unannotated corpus. We compare results from applying this approach to an unannotated Arabic corpus with those achieved by processing the same text in treebank form. The original plan was to use only a morphological analyzer and an unannotated corpus, but experiments suggest that this approach by itself will not be effective for learning the combinatorial potential of Arabic verbs in general. The lower bound on resources for acquiring this information is somewhat higher, apparently requiring a a part-of-speech tagger and chunker for most languages, and a morphological disambiguater for Arabic.
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Considering a resource-light approach to learning verb valencies
| 1,471
|
Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.
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Beyond Sentiment: The Manifold of Human Emotions
| 1,472
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This paper presents the continuation of the work completed by Satori and all. [SCH07] by the realization of an automatic speech recognition system (ASR) for Arabic language based SPHINX 4 system. The previous work was limited to the recognition of the first ten digits, whereas the present work is a remarkable projection consisting in continuous Arabic speech recognition with a rate of recognition of surroundings 96%.
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Realisation d'un systeme de reconnaissance automatique de la parole
arabe base sur CMU Sphinx
| 1,473
|
I pinpoint an interesting similarity between a recent account to rational parsing and the treatment of sequential decisions problems in a dynamical systems approach. I argue that expectation-driven search heuristics aiming at fast computation resembles a high-risk decision strategy in favor of large transition velocities. Hale's rational parser, combining generalized left-corner parsing with informed $\mathrm{A}^*$ search to resolve processing conflicts, explains gardenpath effects in natural sentence processing by misleading estimates of future processing costs that are to be minimized. On the other hand, minimizing the duration of cognitive computations in time-continuous dynamical systems can be described by combining vector space representations of cognitive states by means of filler/role decompositions and subsequent tensor product representations with the paradigm of stable heteroclinic sequences. Maximizing transition velocities according to a high-risk decision strategy could account for a fast race even between states that are apparently remote in representation space.
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The Horse Raced Past: Gardenpath Processing in Dynamical Systems
| 1,474
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This paper describes a context free grammar (CFG) based grammatical relations for Myanmar sentences which combine corpus-based function tagging system. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step to show grammatical relations of Myanmar sentences. In the task of function tagging, which tags the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information, we use Naive Bayesian theory to disambiguate the possible function tags of a word. We apply context free grammar (CFG) to find out the grammatical relations of the function tags. We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences. Experiments show that our analysis achieves a good result with simple sentences and complex sentences.
|
Statistical Function Tagging and Grammatical Relations of Myanmar
Sentences
| 1,475
|
The ability to mimic human notions of semantic distance has widespread applications. Some measures rely only on raw text (distributional measures) and some rely on knowledge sources such as WordNet. Although extensive studies have been performed to compare WordNet-based measures with human judgment, the use of distributional measures as proxies to estimate semantic distance has received little attention. Even though they have traditionally performed poorly when compared to WordNet-based measures, they lay claim to certain uniquely attractive features, such as their applicability in resource-poor languages and their ability to mimic both semantic similarity and semantic relatedness. Therefore, this paper presents a detailed study of distributional measures. Particular attention is paid to flesh out the strengths and limitations of both WordNet-based and distributional measures, and how distributional measures of distance can be brought more in line with human notions of semantic distance. We conclude with a brief discussion of recent work on hybrid measures.
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Distributional Measures of Semantic Distance: A Survey
| 1,476
|
The automatic ranking of word pairs as per their semantic relatedness and ability to mimic human notions of semantic relatedness has widespread applications. Measures that rely on raw data (distributional measures) and those that use knowledge-rich ontologies both exist. Although extensive studies have been performed to compare ontological measures with human judgment, the distributional measures have primarily been evaluated by indirect means. This paper is a detailed study of some of the major distributional measures; it lists their respective merits and limitations. New measures that overcome these drawbacks, that are more in line with the human notions of semantic relatedness, are suggested. The paper concludes with an exhaustive comparison of the distributional and ontology-based measures. Along the way, significant research problems are identified. Work on these problems may lead to a better understanding of how semantic relatedness is to be measured.
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Distributional Measures as Proxies for Semantic Relatedness
| 1,477
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The study of natural language, especially Arabic, and mechanisms for the implementation of automatic processing is a fascinating field of study, with various potential applications. The importance of tools for natural language processing is materialized by the need to have applications that can effectively treat the vast mass of information available nowadays on electronic forms. Among these tools, mainly driven by the necessity of a fast writing in alignment to the actual daily life speed, our interest is on the writing auditors. The morphological and syntactic properties of Arabic make it a difficult language to master, and explain the lack in the processing tools for that language. Among these properties, we can mention: the complex structure of the Arabic word, the agglutinative nature, lack of vocalization, the segmentation of the text, the linguistic richness, etc.
|
Fault detection system for Arabic language
| 1,478
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Modern computational linguistic software cannot produce important aspects of sign language translation. Using some researches we deduce that the majority of automatic sign language translation systems ignore many aspects when they generate animation; therefore the interpretation lost the truth information meaning. Our goals are: to translate written text from any language to ASL animation; to model maximum raw information using machine learning and computational techniques; and to produce a more adapted and expressive form to natural looking and understandable ASL animations. Our methods include linguistic annotation of initial text and semantic orientation to generate the facial expression. We use the genetic algorithms coupled to learning/recognized systems to produce the most natural form. To detect emotion we are based on fuzzy logic to produce the degree of interpolation between facial expressions. Roughly, we present a new expressive language Text Adapted Sign Modeling Language TASML that describes all maximum aspects related to a natural sign language interpretation. This paper is organized as follow: the next section is devoted to present the comprehension effect of using Space/Time/SVO form in ASL animation based on experimentation. In section 3, we describe our technical considerations. We present the general approach we adopted to develop our tool in section 4. Finally, we give some perspectives and future works.
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Toward an example-based machine translation from written text to ASL
using virtual agent animation
| 1,479
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In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma level analysis and generation does not yet focused in Arabic NLP literatures. In the current research, we propose the first non-statistical accurate Arabic lemmatizer algorithm that is suitable for information retrieval (IR) systems. The proposed lemmatizer makes use of different Arabic language knowledge resources to generate accurate lemma form and its relevant features that support IR purposes. As a POS tagger, the experimental results show that, the proposed algorithm achieves a maximum accuracy of 94.8%. For first seen documents, an accuracy of 89.15% is achieved, compared to 76.7% of up to date Stanford accurate Arabic model, for the same, dataset.
|
An Accurate Arabic Root-Based Lemmatizer for Information Retrieval
Purposes
| 1,480
|
In this paper, a supervised learning technique for extracting keyphrases of Arabic documents is presented. The extractor is supplied with linguistic knowledge to enhance its efficiency instead of relying only on statistical information such as term frequency and distance. During analysis, an annotated Arabic corpus is used to extract the required lexical features of the document words. The knowledge also includes syntactic rules based on part of speech tags and allowed word sequences to extract the candidate keyphrases. In this work, the abstract form of Arabic words is used instead of its stem form to represent the candidate terms. The Abstract form hides most of the inflections found in Arabic words. The paper introduces new features of keyphrases based on linguistic knowledge, to capture titles and subtitles of a document. A simple ANOVA test is used to evaluate the validity of selected features. Then, the learning model is built using the LDA - Linear Discriminant Analysis - and training documents. Although, the presented system is trained using documents in the IT domain, experiments carried out show that it has a significantly better performance than the existing Arabic extractor systems, where precision and recall values reach double their corresponding values in the other systems especially for lengthy and non-scientific articles.
|
Arabic Keyphrase Extraction using Linguistic knowledge and Machine
Learning Techniques
| 1,481
|
This paper gives a detail overview about the modified features selection in CRF (Conditional Random Field) based Manipuri POS (Part of Speech) tagging. Selection of features is so important in CRF that the better are the features then the better are the outputs. This work is an attempt or an experiment to make the previous work more efficient. Multiple new features are tried to run the CRF and again tried with the Reduplicated Multiword Expression (RMWE) as another feature. The CRF run with RMWE because Manipuri is rich of RMWE and identification of RMWE becomes one of the necessities to bring up the result of POS tagging. The new CRF system shows a Recall of 78.22%, Precision of 73.15% and F-measure of 75.60%. With the identification of RMWE and considering it as a feature makes an improvement to a Recall of 80.20%, Precision of 74.31% and F-measure of 77.14%.
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Reduplicated MWE (RMWE) helps in improving the CRF based Manipuri POS
Tagger
| 1,482
|
We present CAVaT, a tool that performs Corpus Analysis and Validation for TimeML. CAVaT is an open source, modular checking utility for statistical analysis of features specific to temporally-annotated natural language corpora. It provides reporting, highlights salient links between a variety of general and time-specific linguistic features, and also validates a temporal annotation to ensure that it is logically consistent and sufficiently annotated. Uniquely, CAVaT provides analysis specific to TimeML-annotated temporal information. TimeML is a standard for annotating temporal information in natural language text. In this paper, we present the reporting part of CAVaT, and then its error-checking ability, including the workings of several novel TimeML document verification methods. This is followed by the execution of some example tasks using the tool to show relations between times, events, signals and links. We also demonstrate inconsistencies in a TimeML corpus (TimeBank) that have been detected with CAVaT.
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Analysing Temporally Annotated Corpora with CAVaT
| 1,483
|
Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.
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Using Signals to Improve Automatic Classification of Temporal Relations
| 1,484
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We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and anchors temporal expressions, and a maximum entropy classifier assigns temporal link labels, based on features that include descriptions of associated temporal signal words. USFD2 identified temporal expressions successfully, and correctly classified their type in 90% of cases. Determining the relation between an event and time expression in the same sentence was performed at 63% accuracy, the second highest score in this part of the challenge.
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USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2
| 1,485
|
In this paper we present RTMML, a markup language for the tenses of verbs and temporal relations between verbs. There is a richness to tense in language that is not fully captured by existing temporal annotation schemata. Following Reichenbach we present an analysis of tense in terms of abstract time points, with the aim of supporting automated processing of tense and temporal relations in language. This allows for precise reasoning about tense in documents, and the deduction of temporal relations between the times and verbal events in a discourse. We define the syntax of RTMML, and demonstrate the markup in a range of situations.
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An Annotation Scheme for Reichenbach's Verbal Tense Structure
| 1,486
|
Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are conveyed in text via va rious linguistic mechanisms including the use of expressions such as "before", "after" or "during" that explicitly assert a temporal relation -- temporal signals. In this paper, we investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expres sions in the TimeBank annotated corpus.
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A Corpus-based Study of Temporal Signals
| 1,487
|
This paper describes the University of Sheffield's entry in the 2011 TAC KBP entity linking and slot filling tasks. We chose to participate in the monolingual entity linking task, the monolingual slot filling task and the temporal slot filling tasks. We set out to build a framework for experimentation with knowledge base population. This framework was created, and applied to multiple KBP tasks. We demonstrated that our proposed framework is effective and suitable for collaborative development efforts, as well as useful in a teaching environment. Finally we present results that, while very modest, provide improvements an order of magnitude greater than our 2010 attempt.
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USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding
| 1,488
|
Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. Gold standard temporally-annotated resources are limited in size, which makes research using them difficult. Standards have also evolved over the past decade, so not all temporally annotated data is in the same format. We vastly increase available human-annotated temporal expression resources by converting older format resources to TimeML/TIMEX3. This task is difficult due to differing annotation methods. We present a robust conversion tool and a new, large temporal expression resource. Using this, we evaluate our conversion process by using it as training data for an existing TimeML annotation tool, achieving a 0.87 F1 measure -- better than any system in the TempEval-2 timex recognition exercise.
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Massively Increasing TIMEX3 Resources: A Transduction Approach
| 1,489
|
ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing's online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing's spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in different languages indicated a successful decrease in the number of ASR errors and an improvement in the overall error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor computers.
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Post-Editing Error Correction Algorithm for Speech Recognition using
Bing Spelling Suggestion
| 1,490
|
At the present time, computers are employed to solve complex tasks and problems ranging from simple calculations to intensive digital image processing and intricate algorithmic optimization problems to computationally-demanding weather forecasting problems. ASR short for Automatic Speech Recognition is yet another type of computational problem whose purpose is to recognize human spoken speech and convert it into text that can be processed by a computer. Despite that ASR has many versatile and pervasive real-world applications,it is still relatively erroneous and not perfectly solved as it is prone to produce spelling errors in the recognized text, especially if the ASR system is operating in a noisy environment, its vocabulary size is limited, and its input speech is of bad or low quality. This paper proposes a post-editing ASR error correction method based on MicrosoftN-Gram dataset for detecting and correcting spelling errors generated by ASR systems. The proposed method comprises an error detection algorithm for detecting word errors; a candidate corrections generation algorithm for generating correction suggestions for the detected word errors; and a context-sensitive error correction algorithm for selecting the best candidate for correction. The virtue of using the Microsoft N-Gram dataset is that it contains real-world data and word sequences extracted from the web which canmimica comprehensive dictionary of words having a large and all-inclusive vocabulary. Experiments conducted on numerous speeches, performed by different speakers, showed a remarkable reduction in ASR errors. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor and distributed systems.
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ASR Context-Sensitive Error Correction Based on Microsoft N-Gram Dataset
| 1,491
|
Tree transducers are formal automata that transform trees into other trees. Many varieties of tree transducers have been explored in the automata theory literature, and more recently, in the machine translation literature. In this paper I review T and xT transducers, situate them among related formalisms, and show how they can be used to implement rules for machine translation systems that cover all of the cross-language structural divergences described in Bonnie Dorr's influential article on the topic. I also present an implementation of xT transduction, suitable and convenient for experimenting with translation rules.
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Tree Transducers, Machine Translation, and Cross-Language Divergences
| 1,492
|
WordNet proved that it is possible to construct a large-scale electronic lexical database on the principles of lexical semantics. It has been accepted and used extensively by computational linguists ever since it was released. Inspired by WordNet's success, we propose as an alternative a similar resource, based on the 1987 Penguin edition of Roget's Thesaurus of English Words and Phrases. Peter Mark Roget published his first Thesaurus over 150 years ago. Countless writers, orators and students of the English language have used it. Computational linguists have employed Roget's for almost 50 years in Natural Language Processing, however hesitated in accepting Roget's Thesaurus because a proper machine tractable version was not available. This dissertation presents an implementation of a machine-tractable version of the 1987 Penguin edition of Roget's Thesaurus - the first implementation of its kind to use an entire current edition. It explains the steps necessary for taking a machine-readable file and transforming it into a tractable system. This involves converting the lexical material into a format that can be more easily exploited, identifying data structures and designing classes to computerize the Thesaurus. Roget's organization is studied in detail and contrasted with WordNet's. We show two applications of the computerized Thesaurus: computing semantic similarity between words and phrases, and building lexical chains in a text. The experiments are performed using well-known benchmarks and the results are compared to those of other systems that use Roget's, WordNet and statistical techniques. Roget's has turned out to be an excellent resource for measuring semantic similarity; lexical chains are easily built but more difficult to evaluate. We also explain ways in which Roget's Thesaurus and WordNet can be combined.
|
Roget's Thesaurus as a Lexical Resource for Natural Language Processing
| 1,493
|
Spell-checking is the process of detecting and sometimes providing suggestions for incorrectly spelled words in a text. Basically, the larger the dictionary of a spell-checker is, the higher is the error detection rate; otherwise, misspellings would pass undetected. Unfortunately, traditional dictionaries suffer from out-of-vocabulary and data sparseness problems as they do not encompass large vocabulary of words indispensable to cover proper names, domain-specific terms, technical jargons, special acronyms, and terminologies. As a result, spell-checkers will incur low error detection and correction rate and will fail to flag all errors in the text. This paper proposes a new parallel shared-memory spell-checking algorithm that uses rich real-world word statistics from Yahoo! N-Grams Dataset to correct non-word and real-word errors in computer text. Essentially, the proposed algorithm can be divided into three sub-algorithms that run in a parallel fashion: The error detection algorithm that detects misspellings, the candidates generation algorithm that generates correction suggestions, and the error correction algorithm that performs contextual error correction. Experiments conducted on a set of text articles containing misspellings, showed a remarkable spelling error correction rate that resulted in a radical reduction of both non-word and real-word errors in electronic text. In a further study, the proposed algorithm is to be optimized for message-passing systems so as to become more flexible and less costly to scale over distributed machines.
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Parallel Spell-Checking Algorithm Based on Yahoo! N-Grams Dataset
| 1,494
|
With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google's online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.
|
OCR Post-Processing Error Correction Algorithm using Google Online
Spelling Suggestion
| 1,495
|
We have implemented a system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles' list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough's 65 pairs, which we have also studied. Our Roget's-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget's and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader's Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75%, 82.00% and 74.33% of the questions respectively.
|
Roget's Thesaurus and Semantic Similarity
| 1,496
|
Morris and Hirst present a method of linking significant words that are about the same topic. The resulting lexical chains are a means of identifying cohesive regions in a text, with applications in many natural language processing tasks, including text summarization. The first lexical chains were constructed manually using Roget's International Thesaurus. Morris and Hirst wrote that automation would be straightforward given an electronic thesaurus. All applications so far have used WordNet to produce lexical chains, perhaps because adequate electronic versions of Roget's were not available until recently. We discuss the building of lexical chains using an electronic version of Roget's Thesaurus. We implement a variant of the original algorithm, and explain the necessary design decisions. We include a comparison with other implementations.
|
Not As Easy As It Seems: Automating the Construction of Lexical Chains
Using Roget's Thesaurus
| 1,497
|
This paper presents the steps involved in creating an electronic lexical knowledge base from the 1987 Penguin edition of Roget's Thesaurus. Semantic relations are labelled with the help of WordNet. The two resources are compared in a qualitative and quantitative manner. Differences in the organization of the lexical material are discussed, as well as the possibility of merging both resources.
|
Roget's Thesaurus: a Lexical Resource to Treasure
| 1,498
|
We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmentations as the proportion of boundaries that are not transformed when comparing them using edit distance, essentially using edit distance as a penalty function and scaling penalties by segmentation size. We propose several adapted inter-annotator agreement coefficients which use S that are suitable for segmentation. We show that S is configurable enough to suit a wide variety of segmentation evaluations, and is an improvement upon the state of the art. We also propose using inter-annotator agreement coefficients to evaluate automatic segmenters in terms of human performance.
|
Segmentation Similarity and Agreement
| 1,499
|
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