An automatic morphological analysis system for Indonesian

This thesis reports the creation of SANTI-morf (Sistem Analisis Teks Indonesia – morfologi), a rule-based system that performs morphological annotation for Indonesian. The system has been built across three stages, namely preliminaries, annotation scheme creation (the linguistic aspect of the project), and system implementation (the computational aspect of the project). The preliminary matters covered include the necessary key concepts in morphology and Natural Language Processing (NLP), as well as a concise description of Indonesian morphology (largely based on the two primary reference grammars of Indonesian, Alwi et al. 1998 and Sneddon et al. 2010, together with work in the linguistic literature on Indonesian morphology (e.g. Kridalaksana 1989; Chaer 2008). As part of this preliminary stage, I created a testbed corpus for evaluation purposes. The design of the testbed is justified by considering the design of existing evaluation corpora, such as the testbed used by the English Constraint Grammar or EngCG system (Voutilanen 1992), the British National Corpus (BNC) 1994 evaluation data , and the training data used by MorphInd (Larasati et al. 2011), a morphological analyser (MA) for Indonesian. The dataset for this testbed was created by narrowing down an existing very large bit unbalanced collection of texts (drawn from the Leipzig corpora; see Goldhahn et al. 2012). The initial collection was reduced to a corpus composed of nine domains following the domain categorisation of the BNC) . A set of texts from each domain, proportional in size, was extracted and combined to form a testbed that complies with the design cited informed by the prior literature. The second stage, scheme creation, involved the creation of a new Morphological Annotation Scheme (MAS) for Indonesian, for use in the SANTI-morf system. First, a review of MASs in different languages (Finnish, Turkish, Arabic, Indonesian) as well as the Universal Dependencies MAS identifies the best practices in the field. From these, 15 design principles for the novel MAS were devised. This MAS consists of a morphological tagset, together with comprehensive justification of the morphological analyses used in the system. It achieves full morpheme-level annotation, presenting each morpheme’s orthographic and citation forms in the defined output, accompanied by robust morphological analyses, both formal and functional; to my knowledge, this is the first MAS of its kind for Indonesian. The MAS’s design is based not only on reference grammars of Indonesian and other linguistic sources, but also on the anticipated needs of researchers and other users of texts and corpora annotated using this scheme of analysis. The new MAS aims at The third stage of the project, implementation, consisted of three parts: a benchmarking evaluation exercise, a survey of frameworks and tools, leading ultimately to the actual implementation and evaluation of SANTI-morf. MorphInd (Larasati et al. 2012) is the prior state-of-the-art MA for Indonesian. That being the case, I evaluated MorphInd’s performance against the aforementioned testbed, both as just5ification of the need for an improved system, and to serve as a benchmark for SANTI-morf. MorphInd scored 93% on lexical coverage and 89% on tagging accuracy. Next, I surveyed existing MAs frameworks and tools. This survey justifies my choice for the rule-based approach (inspired by Koskenniemi’s 1983 Two Level Morphology, and NooJ (Silberztein 2S003) as respectively the framework and the software tool for SANTI-morf. After selection of this approach and tool, the language resources that constitute the SANTI-morf system were created. These are, primarily, a number of lexicons and sets of analysis rules, as well as necessary NooJ system configuration files. SANTI-morf’s 3 lexicon files (in total 86,590 entries) and 15 rule files (in total 659 rules) are organised into four modules, namely the Annotator, the Guesser, the Improver and the Disambiguator. These modules are applied one after another in a pipeline. The Annotator provides initial morpheme-level annotation for Indonesian words by identifying their having been built according to various morphological processes (affixation, reduplication, compounding, and cliticisation). The Guesser ensures that words not covered by the Annotator, because they are not covered by its lexicons, receive best guesses as to the correct analysis from the application of a set of probable but not exceptionless rules. The Improver improves the existing annotation, by adding probable analyses that the Annotator might have missed. Finally, the Disambiguator resolves ambiguities, that is, words for which the earlier elements of the pipeline have generated two or more possible analyses in terms of the morphemes identified or their annotation. NooJ annotations are saved in a binary file, but for evaluation purposes, plain-text output is required. I thus developed a system for data export using an in-NooJ mapping to and from a modified, exportable expression of the MAS, and wrote a small program to enable re-conversion of the output in plain-text format. For purposes of the evaluation, I created a 10,000 -word gold-standard SANTI-morf manually-annotated dataset. The outcome of the evaluation is that SANTI-morf has 100% coverage (because a best-guess analysis is always provided for unrecognised word forms), and 99% precision and recall for the morphological annotations, with a 1% rate of remaining ambiguity in the final output. SANTI-morf is thus shown to present a number of advancements over MorphInd, the state-of-the-art MA for Indonesian, exhibiting more robust annotation and better coverage. Other performance indicators, namely the high precision and recall, make SANTI-morf a concrete advance in the field of automated morphological annotation for Indonesian, and in consequence a substantive contribution to the field of Indonesian linguistics overall

Multi-word unit processing in machine translation. Developing and using language resources for multi-word unit processing in machine translation

2011 - 2012XI n.s

Discourse analysis of arabic documents and application to automatic summarization

Dans un discours, les textes et les conversations ne sont pas seulement une juxtaposition de mots et de phrases. Ils sont plutôt organisés en une structure dans laquelle des unités de discours sont liées les unes aux autres de manière à assurer à la fois la cohérence et la cohésion du discours. La structure du discours a montré son utilité dans de nombreuses applications TALN, y compris la traduction automatique, la génération de texte et le résumé automatique. L'utilité du discours dans les applications TALN dépend principalement de la disponibilité d'un analyseur de discours performant. Pour aider à construire ces analyseurs et à améliorer leurs performances, plusieurs ressources ont été annotées manuellement par des informations de discours dans des différents cadres théoriques. La plupart des ressources disponibles sont en anglais. Récemment, plusieurs efforts ont été entrepris pour développer des ressources discursives pour d'autres langues telles que le chinois, l'allemand, le turc, l'espagnol et le hindi. Néanmoins, l'analyse de discours en arabe standard moderne (MSA) a reçu moins d'attention malgré le fait que MSA est une langue de plus de 422 millions de locuteurs dans 22 pays. Le sujet de thèse s'intègre dans le cadre du traitement automatique de la langue arabe, plus particulièrement, l'analyse de discours de textes arabes. Cette thèse a pour but d'étudier l'apport de l'analyse sémantique et discursive pour la génération de résumé automatique de documents en langue arabe. Pour atteindre cet objectif, nous proposons d'étudier la théorie de la représentation discursive segmentée (SDRT) qui propose un cadre logique pour la représentation sémantique de phrases ainsi qu'une représentation graphique de la structure du texte où les relations de discours sont de nature sémantique plutôt qu'intentionnelle. Cette théorie a été étudiée pour l'anglais, le français et l'allemand mais jamais pour la langue arabe. Notre objectif est alors d'adapter la SDRT à la spécificité de la langue arabe afin d'analyser sémantiquement un texte pour générer un résumé automatique. Nos principales contributions sont les suivantes : Une étude de la faisabilité de la construction d'une structure de discours récursive et complète de textes arabes. En particulier, nous proposons : Un schéma d'annotation qui couvre la totalité d'un texte arabe, dans lequel chaque constituant est lié à d'autres constituants. Un document est alors représenté par un graphe acyclique orienté qui capture les relations explicites et les relations implicites ainsi que des phénomènes de discours complexes, tels que l'attachement, la longue distance du discours pop-ups et les dépendances croisées. Une nouvelle hiérarchie des relations de discours. Nous étudions les relations rhétoriques d'un point de vue sémantique en se concentrant sur leurs effets sémantiques et non pas sur la façon dont elles sont déclenchées par des connecteurs de discours, qui sont souvent ambigües en arabe. o une analyse quantitative (en termes de connecteurs de discours, de fréquences de relations, de proportion de relations implicites, etc.) et une analyse qualitative (accord inter-annotateurs et analyse des erreurs) de la campagne d'annotation. Un outil d'analyse de discours où nous étudions à la fois la segmentation automatique de textes arabes en unités de discours minimales et l'identification automatique des relations explicites et implicites du discours. L'utilisation de notre outil pour résumer des textes arabes. Nous comparons la représentation de discours en graphes et en arbres pour la production de résumés.Within a discourse, texts and conversations are not just a juxtaposition of words and sentences. They are rather organized in a structure in which discourse units are related to each other so as to ensure both discourse coherence and cohesion. Discourse structure has shown to be useful in many NLP applications including machine translation, natural language generation and language technology in general. The usefulness of discourse in NLP applications mainly depends on the availability of powerful discourse parsers. To build such parsers and improve their performances, several resources have been manually annotated with discourse information within different theoretical frameworks. Most available resources are in English. Recently, several efforts have been undertaken to develop manually annotated discourse information for other languages such as Chinese, German, Turkish, Spanish and Hindi. Surprisingly, discourse processing in Modern Standard Arabic (MSA) has received less attention despite the fact that MSA is a language with more than 422 million speakers in 22 countries. Computational processing of Arabic language has received a great attention in the literature for over twenty years. Several resources and tools have been built to deal with Arabic non concatenative morphology and Arabic syntax going from shallow to deep parsing. However, the field is still very vacant at the layer of discourse. As far as we know, the sole effort towards Arabic discourse processing was done in the Leeds Arabic Discourse Treebank that extends the Penn Discourse TreeBank model to MSA. In this thesis, we propose to go beyond the annotation of explicit relations that link adjacent units, by completely specifying the semantic scope of each discourse relation, making transparent an interpretation of the text that takes into account the semantic effects of discourse relations. In particular, we propose the first effort towards a semantically driven approach of Arabic texts following the Segmented Discourse Representation Theory (SDRT). Our main contributions are: A study of the feasibility of building a recursive and complete discourse structures of Arabic texts. In particular, we propose: An annotation scheme for the full discourse coverage of Arabic texts, in which each constituent is linked to other constituents. A document is then represented by an oriented acyclic graph, which captures explicit and implicit relations as well as complex discourse phenomena, such as long-distance attachments, long-distance discourse pop-ups and crossed dependencies. A novel discourse relation hierarchy. We study the rhetorical relations from a semantic point of view by focusing on their effect on meaning and not on how they are lexically triggered by discourse connectives that are often ambiguous, especially in Arabic. A thorough quantitative analysis (in terms of discourse connectives, relation frequencies, proportion of implicit relations, etc.) and qualitative analysis (inter-annotator agreements and error analysis) of the annotation campaign. An automatic discourse parser where we investigate both automatic segmentation of Arabic texts into elementary discourse units and automatic identification of explicit and implicit Arabic discourse relations. An application of our discourse parser to Arabic text summarization. We compare tree-based vs. graph-based discourse representations for producing indicative summaries and show that the full discourse coverage of a document is definitively a plus

On the evaluation and improvement of arabic wordnet coverage and usability

The final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We would like to thank Manuel Montes-y-Gomez (INAOE-Puebla, Mexico) and Sandra Garcia-Blasco (Bitsnbrain, Spain) for their feedback on the work presented in Sect. 2.4. We would like finally to thank Violetta Cavalli-Sforza (Al Akhawayn University in Ifrane, Morocco) for having reviewed the linguistic level of the entire document.Abouenour, L.; Bouzoubaa, K.; Rosso, P. (2013). On the evaluation and improvement of arabic wordnet coverage and usability. Language Resources and Evaluation. 47(3):891-917. https://doi.org/10.1007/s10579-013-9237-0S891917473Abbès, R., Dichy, J., & Hassoun, M. (2004). The architecture of a standard Arabic lexical database: Some figures, ratios and categories from the DIINAR.1 source program. In Workshop on computational approaches to Arabic script-based languages, Coling 2004. Geneva, Switzerland.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009a). Structure-based evaluation of an Arabic semantic query expansion using the JIRS passage retrieval system. In Proceedings of the workshop on computational approaches to Semitic languages, E-ACL-2009, Athens, Greece, March.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009b). Three-level approach for passage retrieval in Arabic question/answering systems. In Proceedings of the 3rd international conference on Arabic language processing CITALA’09, Rabat, Morocco, May, 2009.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010a). An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering. Special Issue in the International Journal on Information and Communication Technologies/IEEE. June.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2010b). Using the YAGO ontology as a resource for the enrichment of named entities in Arabic WordNet. In Workshop LR & HLT for semitic languages, LREC’10. Malta. May, 2010.Ahonen-Myka, H. (2002). Discovery of frequent word sequences in text. In Proceedings of the ESF exploratory workshop on pattern detection and discovery (pp. 180–189). London, UK: Springer.Al Khalifa, M., & Rodríguez, H. (2009). Automatically extending NE coverage of Arabic WordNet using Wikipedia. In Proceedings of the 3rd international conference on Arabic language processing CITALA’09, May, Rabat, Morocco.Alotaiby, F., Alkharashi, I., & Foda, S. (2009). Processing large Arabic text corpora: Preliminary analysis and results. In Proceedings of the second international conference on Arabic language resources and tools (pp. 78–82), Cairo, Egypt.Baker, C. F., Fillmore, C. J., & Cronin, B. (2003). The structure of the FrameNet database. International Journal of Lexicography, 16(3), 281–296.Baldwin, T., Pool, P., & Colowick, S. M. (2010). PanLex and LEXTRACT: Translating all words of all languages of the world. In Proceedings of Coling 2010, demonstration volume (pp. 37–40), Beijing.Benajiba, Y., Diab, M., & Rosso, P. (2009). Using language independent and language specific features to enhance Arabic named entity recognition. In IEEE transactions on audio, speech and language processing. Special Issue on Processing Morphologically Rich Languages, 17(5), 2009.Benajiba, Y., Rosso, P., & Lyhyaoui, A. (2007). Implementation of the ArabiQA question answering system’s components. In Proceedings of workshop on Arabic natural language processing, 2nd Information Communication Technologies int. symposium, ICTIS-2007, April 3–5, Fez, Morocco.Benoît, S., & Darja, F. (2008). Building a free French WordNet from multilingual resources. Workshop on Ontolex 2008, LREC’08, June, Marrakech, Morocco.Black, W., Elkateb, S., Rodriguez, H, Alkhalifa, M., Vossen, P., Pease, A., et al. (2006). Introducing the Arabic WordNet project. In Proceedings of the third international WordNet conference. Sojka, Choi: Fellbaum & Vossen (eds).Boudelaa, S., & Gaskell, M. G. (2002). A reexamination of the default system for Arabic plurals. Language and Cognitive Processes, 17, 321–343.Brini, W., Ellouze & M., Hadrich, B. L. (2009a). QASAL: Un système de question-réponse dédié pour les questions factuelles en langue Arabe. In 9th Journées Scientifiques des Jeunes Chercheurs en Génie Electrique et Informatique, Tunisia.Brini, W., Trigui, O., Ellouze, M., Mesfar, S., Hadrich, L., & Rosso, P. (2009b). Factoid and definitional Arabic question answering system. In Post-proceedings of NOOJ-2009, June 8–10, Tozeur, Tunisia.Buscaldi, D., Rosso, P., Gómez, J. M., & Sanchis, E. (2010). Answering questions with an n-gram based passage retrieval engine. Journal of Intelligent Information Systems, 34(2), 113–134.Costa, R. P., & Seco, N. (2008). Hyponymy extraction and Web search behavior analysis based on query reformulation. In Proceedings of the 11th Ibero-American conference on AI: advances in artificial intelligence (pp. 1–10).Denicia-carral, C., Montes-y-Gõmez, M., Villaseñor-pineda, L., & Hernandez, R. G. (2006). A text mining approach for definition question answering. In Proceedings of the 5th international conference on natural language processing, FinTal’2006, Turku, Finland.Diab, M. T. (2004). Feasibility of bootstrapping an Arabic Wordnet leveraging parallel corpora and an English Wordnet. In Proceedings of the Arabic language technologies and resources, NEMLAR, Cairo, Egypt.El Amine, M. A. (2009). Vers une interface pour l’enrichissement des requêtes en arabe dans un système de recherche d’information. In Proceedings of the 2nd conférence internationale sur l’informatique et ses applications (CIIA’09), May 3–4, Saida, Algeria.Elghamry, K. (2008). Using the web in building a corpus-based hypernymy-hyponymy Lexicon with hierarchical structure for Arabic. In Proceedings of the 6th international conference on informatics and systems, INFOS 2008. Cairo, Egypt.Elkateb, S., Black, W., Vossen, P., Farwell, D., Rodríguez, H., Pease, A., et al. (2006). Arabic WordNet and the challenges of Arabic. In Proceedings of Arabic NLP/MT conference, London, UK.Fellbaum, C. (Ed.). (1998). WordNet: An electronic lexical database. MA: MIT Press.García-Blasco, S., Danger, R., & Rosso, P. (2010). Drug–drug interaction detection: A new approach based on maximal frequent sequences. Sociedad Española para el Procesamiento del Lenguaje Natural, SEPLN, 45, 263–266.García-Hernández, R. A. (2007). Algoritmos para el descubrimiento de patrones secuenciales maximales. Ph.D. Thesis, INAOE. September, Mexico.García-Hernández, R. A., Martínez Trinidad, J. F., & Carrasco-ochoa, J. A. (2010). Finding maximal sequential patterns in text document collections and single documents. Informatica, 34(1), 93–101.Goweder, A., & De Roeck, A. (2001). Assessment of a significant Arabic corpus. In Proceedings of the Arabic NLP workshop at ACL/EACL, (pp. 73–79), Toulouse, France.Graff, D. (2007). Arabic Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Graff, D., Kong, J., Chen, K., & Maeda, K. (2007). English Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Hammou, B., Abu-salem, H., Lytinen, S., & Evens, M. (2002). QARAB: A question answering system to support the Arabic language. In Proceedings of the workshop on computational approaches to Semitic languages, ACL, (pp. 55–65), Philadelphia.Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics, COLING ‘92 (vol. 2, pp. 539–545).Kanaan, G., Hammouri, A., Al-Shalabi, R., & Swalha, M. (2009). A new question answering system for the Arabic language. American Journal of Applied Sciences, 6(4), 797–805.Kim, H., Chen, S., & Veale, T. (2006). Analogical reasoning with a synergy of HowNet and WordNet. In Proceedings of GWC’2006, the 3rd global WordNet conference, January, Cheju, Korea.Kipper-Schuler, K. (2006). VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. Thesis.Mohammed, F. A., Nasser, K., & Harb, H. M. (1993). A knowledge-based Arabic question answering system (AQAS). In ACM SIGART bulletin (pp. 21–33).Niles, I., & Pease, A. (2001). Towards a standard upper ontology. In Proceedings of FOIS-2 (pp. 2–9), Ogunquit, Maine.Niles, I., & Pease, A. (2003). Linking lexicons and ontologies: Mapping WordNet to the suggested upper merged ontology. In Proceedings of the 2003 international conference on information and knowledge engineering, Las Vegas, Nevada.Ortega-Mendoza, R. M., Villaseñor-pineda, L., & Montes-y-Gõmez, M. (2007). Using lexical patterns to extract hyponyms from the Web. In Proceedings of the Mexican international conference on artificial intelligence MICAI 2007. November, Aguascalientes, Mexico. Lecture Notes in Artificial Intelligence 4827. Berlin: Springer.Palmer, M., P. Kingsbury, & D. Gildea. (2005). The proposition bank: An annotated corpus of semantic roles. Computational Linguistics, 21. USA: MIT Press.Pantel, P., & Pennacchiotti, M. (2006). Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of conference on computational linguistics association for computational linguistics, (pp. 113–120), Sydney, Australia.Rodriguez, H., Farwell, D., Farreres, J., Bertran, M., Alkhalifa, M., & Martí, A. (2008a). Arabic WordNet: Semi-automatic extensions using Bayesian Inference. In Proceedings of the the 6th conference on language resources and evaluation LREC2008, May, Marrakech, Morocco.Rodriguez, H., Farwell, D., Farreres, J., Bertran, M., Alkhalifa, M., Mart., M., et al. (2008b). Arabic WordNet: Current state and future extensions. In Proceedings of the fourth global WordNet conference, January 22–25, Szeged, Hungary.Sharaf, A. M. (2009). The Qur’an annotation for text mining. First year transfer report. School of Computing, Leeds University. December.Snow, R., Jurafsky, D., & Andrew, Y. N. (2005). Learning syntactic patterns for automatic hypernym discovery. In Lawrence K. Saul et al. (Eds.), Advances in neural information processing systems, 17. Cambridge, MA: MIT Press.Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). YAGO: A core of semantic knowledge unifying WordNet and Wikipedia. In Proceedings of 16th international World Wide Web conference WWW’2007, (pp. 697–706), May, Banff, Alberta, Canada: ACM Press.Tjong Kim Sang, E., & Hofmann, K. (2007). Automatic extraction of Dutch hypernym–hyponym pairs. In Proceedings of CLIN-2006, Leuven, Belgium.Toral, A., Munoz, R., & Monachini, M. (2008). Named entity WordNet. In Proceedings of the Sixth international conference on language resources and evaluation (LREC’08), Marrakech, Morocco.Vossen, P. (Ed.). (1998). EuroWordNet, a multilingual database with lexical semantic networks. The Netherlands: Kluwer.Wagner, A. (2005). Learning thematic role relations for lexical semantic nets. Ph.D. Thesis, University of Tübingen, 2005

Fine-grained Arabic named entity recognition

This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in the number of semantic classes and the decrease in semantic differences between fine-grained classes. Our approach to developing fine-grained NER relies on two different supervised Machine Learning (ML) technologies (i.e. Maximum Entropy 'ME' and Conditional Random Fields 'CRF'), which require annotated training data in order to learn by extracting informative features. We develop a methodology which exploit the richness of Arabic Wikipedia (A W) in order to create a scalable fine-grained lexical resource and a corpus automatically. Moreover, two gold-standard created corpora from different genres were also developed to perform comparable evaluation. The thesis also developed a new approach to feature representation by relying on the dependency structure of the sentence to overcome the limitation of traditional window-based (i.e. n-gram) representation. Furthermore, by exploiting the richness of unannotated textual data to extract global informative features using word-level clustering technique was also achieved. Each contribution was evaluated via controlled experiment and reported using three commonly applied metrics, i.e. precision, recall and harmonic F-measure

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

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

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

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

Contribution à l’amélioration de la recherche d’information par utilisation des méthodes sémantiques: application à la langue arabe

Un système de recherche d’information est un ensemble de programmes et de modules qui sert à interfacer avec l’utilisateur, pour prendre et interpréter une requête, faire la recherche dans l’index et retourner un classement des documents sélectionnés à cet utilisateur. Cependant le plus grand challenge de ce système est qu’il doit faire face au grand volume d’informations multi modales et multilingues disponibles via les bases documentaires ou le web pour trouver celles qui correspondent au mieux aux besoins des utilisateurs. A travers ce travail, nous avons présenté deux contributions. Dans la première nous avons proposé une nouvelle approche pour la reformulation des requêtes dans le contexte de la recherche d’information en arabe. Le principe est donc de représenter la requête par un arbre sémantique pondéré pour mieux identifier le besoin d'information de l'utilisateur, dont les nœuds représentent les concepts (synsets) reliés par des relations sémantiques. La construction de cet arbre est réalisée par la méthode de la Pseudo-Réinjection de la Pertinence combinée à la ressource sémantique du WordNet Arabe. Les résultats expérimentaux montrent une bonne amélioration dans les performances du système de recherche d’information. Dans la deuxième contribution, nous avons aussi proposé une nouvelle approche pour la construction d’une collection de test de recherche d’information arabe. L'approche repose sur la combinaison de la méthode de la stratégie de Pooling utilisant les moteurs de recherches et l’algorithme Naïve-Bayes de classification par l’apprentissage automatique. Pour l’expérimentation nous avons créé une nouvelle collection de test composée d’une base documentaire de 632 documents et de 165 requêtes avec leurs jugements de pertinence sous plusieurs topics. L’expérimentation a également montré l’efficacité du classificateur Bayésien pour la récupération de pertinences des documents, encore plus, il a réalisé des bonnes performances après l’enrichissement sémantique de la base documentaire par le modèle word2vec

A Computational Lexicon and Representational Model for Arabic Multiword Expressions

The phenomenon of multiword expressions (MWEs) is increasingly recognised as a serious and challenging issue that has attracted the attention of researchers in various language-related disciplines. Research in these many areas has emphasised the primary role of MWEs in the process of analysing and understanding language, particularly in the computational treatment of natural languages. Ignoring MWE knowledge in any NLP system reduces the possibility of achieving high precision outputs. However, despite the enormous wealth of MWE research and language resources available for English and some other languages, research on Arabic MWEs (AMWEs) still faces multiple challenges, particularly in key computational tasks such as extraction, identification, evaluation, language resource building, and lexical representations. This research aims to remedy this deficiency by extending knowledge of AMWEs and making noteworthy contributions to the existing literature in three related research areas on the way towards building a computational lexicon of AMWEs. First, this study develops a general understanding of AMWEs by establishing a detailed conceptual framework that includes a description of an adopted AMWE concept and its distinctive properties at multiple linguistic levels. Second, in the use of AMWE extraction and discovery tasks, the study employs a hybrid approach that combines knowledge-based and data-driven computational methods for discovering multiple types of AMWEs. Third, this thesis presents a representative system for AMWEs which consists of multilayer encoding of extensive linguistic descriptions. This project also paves the way for further in-depth AMWE-aware studies in NLP and linguistics to gain new insights into this complicated phenomenon in standard Arabic. The implications of this research are related to the vital role of the AMWE lexicon, as a new lexical resource, in the improvement of various ANLP tasks and the potential opportunities this lexicon provides for linguists to analyse and explore AMWE phenomena

Multiword expression processing: A survey

Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives