27 research outputs found

    Syntaxe computationnelle du hongrois : de l'analyse en chunks à la sous-catégorisation verbale

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    We present the creation of two resources for Hungarian NLP applications: a rule-based shallow parser and a database of verbal subcategorization frames. Hungarian, as a non-configurational language with a rich morphology, presents specific challenges for NLP at the level of morphological and syntactic processing. While efficient and precise morphological analyzers are already available, Hungarian is under-resourced with respect to syntactic analysis. Our work aimed at overcoming this problem by providing resources for syntactic processing. Hungarian language is characterized by a rich morphology and a non-configurational encoding of grammatical functions. These features imply that the syntactic processing of Hungarian has to rely on morphological features rather than on constituent order. The broader interest of our undertaking is to propose representations and methods that are adapted to these specific characteristics, and at the same time are in line with state of the art research methodologies. More concretely, we attempt to adapt current results in argument realization and lexical semantics to the task of labeling sentence constituents according to their syntactic function and semantic role in Hungarian. Syntax and semantics are not completely independent modules in linguistic analysis and language processing: it has been known for decades that semantic properties of words affect their syntactic distribution. Within the syntax-semantics interface, the field of argument realization deals with the (partial or complete) prediction of verbal subcategorization from semantic properties. Research on verbal lexical semantics and semantically motivated mapping has been concentrating on predicting the syntactic realization of arguments, taking for granted (either explicitly or implicitly) that the distinction between arguments and adjuncts is known, and that adjuncts' syntactic realization is governed by productive syntactic rules, not lexical properties. However, besides the correlation between verbal aspect or actionsart and time adverbs (e.g. Vendler, 1967 or Kiefer, 1992 for Hungarian), the distribution of adjuncts among verbs or verb classes did not receive significant attention, especially within the lexical semantics framework. We claim that contrary to the widely shared presumption, adjuncts are often not fully productive. We therefore propose a gradual notion of productivity, defined in relation to Levin-type lexical semantic verb classes (Levin, 1993; Levin and Rappaport-Hovav, 2005). The definition we propose for the argument-adjunct dichotomy is based on evidence from Hungarian and exploits the idea that lexical semantics not only influences complement structure but is the key to the argument-adjunct distinction and the realization of adjunctsLa linguistique informatique est un domaine de recherche qui se concentre sur les méthodes et les perspectives de la modélisation formelle (statistique ou symbolique) de la langue naturelle. La linguistique informatique, tout comme la linguistique théorique, est une discipline fortement modulaire : les niveaux d'analyse linguistique comprennent la segmentation, l'analyse morphologique, la désambiguïsation, l'analyse syntaxique et sémantique. Tandis qu'un nombre d'outils existent déjà pour les traitements de bas niveau (analyse morphologique, étiquetage grammatical), le hongrois peut être considéré comme une langue peu doté pour l'analyse syntaxique et sémantique. Le travail décrit dans la présente thèse vise à combler ce manque en créant des ressources pour le traitement syntaxique du hongrois : notamment, un analyseur en chunks et une base de données lexicale de schémas de sous-catégorisation verbale. La première partie de la recherche présentée ici se concentre sur la création d'un analyseur syntaxique de surface (ou analyseur en chunks) pour le hongrois. La sortie de l'analyseur de surface est conçue pour servir d'entrée pour un traitement ultérieur visant à annoter les relations de dépendance entre le prédicat et ses compléments essentiels et circonstanciels. L'analyseur profond est mis en œuvre dans NooJ (Silberztein, 2004) en tant qu'une cascade de grammaires. Le deuxième objectif de recherche était de proposer une représentation lexicale pour la structure argumentale en hongrois. Cette représentation doit pouvoir gérer la vaste gamme de phénomènes qui échappent à la dichotomie traditionnelle entre un complément essentiel et un circonstanciel (p. ex. des structures partiellement productives, des écarts entre la prédictibilité syntaxique et sémantique). Nous avons eu recours à des résultats de la recherche récente sur la réalisation d'arguments et choisi un cadre qui répond à nos critères et qui est adaptable à une langue non-configurationnelle. Nous avons utilisé la classification sémantique de Levin (1993) comme modèle. Nous avons adapté les notions relatives à cette classification, à savoir celle de la composante sémantique et celle de l'alternance syntaxique, ainsi que la méthodologie d'explorer et de décrire le comportement des prédicats à l'aide de cette représentation, à la tâche de construire une représentation lexicale des verbes dans une langue non-configurationnelle. La première étape consistait à définir les règles de codage et de construire un vaste base de données lexicale pour les verbes et leurs compléments. Par la suite, nous avons entrepris deux expériences pour l'enrichissement de ce lexique avec des informations sémantiques lexicales afin de formaliser des généralisations syntaxiques et sémantiques pertinentes sur les classes de prédicats sous-jacentes. La première approche que nous avons testée consistait en une élaboration manuelle de classification de verbes en fonction de leur structure de compléments et de l'attribution de rôles sémantiques à ces compléments. Nous avons cherché la réponse aux questions suivantes: quelles sont les composants sémantiques pertinents pour définir une classification sémantique des prédicats hongrois? Quelles sont les implications syntaxiques spécifiques à ces classes? Et, plus généralement, quelle est la nature des alternances spécifiques aux classes verbales en hongrois ? Dans la phase finale de la recherche, nous avons étudié le potentiel de l'acquisition automatique pour extraire des classes de verbes à partir de corpus. Nous avons effectué une classification non supervisée, basée sur des données distributionnelles, pour obtenir une classification sémantique pertinente des verbes hongrois. Nous avons également testé la méthode de classification non supervisée sur des données françaises

    Exploring formal models of linguistic data structuring. Enhanced solutions for knowledge management systems based on NLP applications

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    2010 - 2011The principal aim of this research is describing to which extent formal models for linguistic data structuring are crucial in Natural Language Processing (NLP) applications. In this sense, we will pay particular attention to those Knowledge Management Systems (KMS) which are designed for the Internet, and also to the enhanced solutions they may require. In order to appropriately deal with this topics, we will describe how to achieve computational linguistics applications helpful to humans in establishing and maintaining an advantageous relationship with technologies, especially with those technologies which are based on or produce man-machine interactions in natural language. We will explore the positive relationship which may exist between well-structured Linguistic Resources (LR) and KMS, in order to state that if the information architecture of a KMS is based on the formalization of linguistic data, then the system works better and is more consistent. As for the topics we want to deal with, frist of all it is indispensable to state that in order to structure efficient and effective Information Retrieval (IR) tools, understanding and formalizing natural language combinatory mechanisms seems to be the first operation to achieve, also because any piece of information produced by humans on the Internet is necessarily a linguistic act. Therefore, in this research work we will also discuss the NLP structuring of a linguistic formalization Hybrid Model, which we hope will prove to be a useful tool to support, improve and refine KMSs. More specifically, in section 1 we will describe how to structure language resources implementable inside KMSs, to what extent they can improve the performance of these systems and how the problem of linguistic data structuring is dealt with by natural language formalization methods. In section 2 we will proceed with a brief review of computational linguistics, paying particular attention to specific software packages such Intex, Unitex, NooJ, and Cataloga, which are developed according to Lexicon-Grammar (LG) method, a linguistic theory established during the 60’s by Maurice Gross. In section 3 we will describe some specific works useful to monitor the state of the art in Linguistic Data Structuring Models, Enhanced Solutions for KMSs, and NLP Applications for KMSs. In section 4 we will cope with problems related to natural language formalization methods, describing mainly Transformational-Generative Grammar (TGG) and LG, plus other methods based on statistical approaches and ontologies. In section 5 we will propose a Hybrid Model usable in NLP applications in order to create effective enhanced solutions for KMSs. Specific features and elements of our hybrid model will be shown through some results on experimental research work. The case study we will present is a very complex NLP problem yet little explored in recent years, i.e. Multi Word Units (MWUs) treatment. In section 6 we will close our research evaluating its results and presenting possible future work perspectives. [edited by author]X n.s

    Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

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    2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s

    Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

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    Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

    A rules based system for named entity recognition in modern standard Arabic

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    The amount of textual information available electronically has made it difficult for many users to find and access the right information within acceptable time. Research communities in the natural language processing (NLP) field are developing tools and techniques to alleviate these problems and help users in exploiting these vast resources. These techniques include Information Retrieval (IR) and Information Extraction (IE). The work described in this thesis concerns IE and more specifically, named entity extraction in Arabic. The Arabic language is of significant interest to the NLP community mainly due to its political and economic significance, but also due to its interesting characteristics. Text usually contains all kinds of names such as person names, company names, city and country names, sports teams, chemicals and lots of other names from specific domains. These names are called Named Entities (NE) and Named Entity Recognition (NER), one of the main tasks of IE systems, seeks to locate and classify automatically these names into predefined categories. NER systems are developed for different applications and can be beneficial to other information management technologies as it can be built over an IR system or can be used as the base module of a Data Mining application. In this thesis we propose an efficient and effective framework for extracting Arabic NEs from text using a rule based approach. Our approach makes use of Arabic contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type. Morphological information is used to detect the part of speech of each word given to the morphological analyzer. Subsequently we developed and implemented our rules in order to recognise each position of the named entity. Finally, our system implementation, evaluation metrics and experimental results are presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    A Rules Based System for Named Entity Recognition in Modern Standard Arabic

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    The amount of textual information available electronically has made it difficult formany users to find and access the right information within acceptable time. Researchcommunities in the natural language processing (NLP) field are developing tools andtechniques to alleviate these problems and help users in exploiting these vast resources.These techniques include Information Retrieval (IR) and Information Extraction (IE). Thework described in this thesis concerns IE and more specifically, named entity extraction inArabic. The Arabic language is of significant interest to the NLP community mainly due toits political and economic significance, but also due to its interesting characteristics.Text usually contains all kinds of names such as person names, company names,city and country names, sports teams, chemicals and lots of other names from specificdomains. These names are called Named Entities (NE) and Named Entity Recognition(NER), one of the main tasks of IE systems, seeks to locate and classify automaticallythese names into predefined categories. NER systems are developed for differentapplications and can be beneficial to other information management technologies as it canbe built over an IR system or can be used as the base module of a Data Mining application.In this thesis we propose an efficient and effective framework for extracting Arabic NEsfrom text using a rule based approach. Our approach makes use of Arabic contextual andmorphological information to extract named entities. The context is represented by meansof words that are used as clues for each named entity type. Morphological information isused to detect the part of speech of each word given to the morphological analyzer.Subsequently we developed and implemented our rules in order to recognise each positionof the named entity. Finally, our system implementation, evaluation metrics andexperimental results are presented

    Acta Cybernetica : Volume 23. Number 4.

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    II. Magyar Számítógépes Nyelvészeti Konferencia

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