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Syntactic and Semantic Impact of Prepositions in Machine Translation : An Empirical Study of French-English Translation of Prepositions ‘à’, ‘de’ and ‘en’
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In: Human Language Technology. Challenges for Computer Science and Linguistics 8th Language and Technology Conference, LTC 2017, Poznań, Poland, November 17–19, 2017, Revised Selected Papers ; 8th Language and Technology Conference (LTC 2017) ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-03091307 ; Human Language Technology. Challenges for Computer Science and Linguistics 8th Language and Technology Conference, LTC 2017, Poznań, Poland, November 17–19, 2017, Revised Selected Papers, 12598, pp.273-287, 2020, Lecture Notes in Computer Science, 978-3-030-66526-5. ⟨10.1007/978-3-030-66527-2_20⟩ (2020)
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Pomset logic: a logical and grammatical alternative to the Lambek calculus
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In: https://hal.archives-ouvertes.fr/hal-02431876 ; 2020 (2020)
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Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About?
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In: Proceedings of the Workshop on Insights from Negative Results in NLP ; Workshop on Insights from Negative Results in NLP ; https://hal.mines-ales.fr/hal-02986882 ; Workshop on Insights from Negative Results in NLP, Nov 2020, Online, Dominican Republic. ⟨10.18653/v1/2020.insights-1.11⟩ ; https://insights-workshop.github.io/ (2020)
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A computational account of virtual travelers in the Montagovian generative lexicon
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In: The Semantics of Dynamic Space in French ; https://hal.archives-ouvertes.fr/hal-02093536 ; Michel Aurnague; Dejan Stosic. The Semantics of Dynamic Space in French, John Benjamins, pp.407-450, 2019, Part IV. Formal and computational aspects of motion-based narrations, 9789027203205. ⟨10.1075/hcp.66.09lef⟩ ; https://benjamins.com/catalog/hcp.66.09lef (2019)
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Semantic Indexing of French Biomedical Data Resources
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In: Project Repository Journal ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-02360615 ; Project Repository Journal, 3, pp.16-19, 2019 ; https://www.europeandissemination.eu/the-project-repository-journal (2019)
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A novel framework for biomedical entity sense induction
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In: ISSN: 1532-0464 ; EISSN: 1532-0480 ; Journal of Biomedical Informatics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01851988 ; Journal of Biomedical Informatics, Elsevier, 2018, 84, pp.31-41. ⟨10.1016/j.jbi.2018.06.007⟩ (2018)
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Cheap, Fast and Good! Voting Games with a Purpose
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In: Games4NLP: Games and Gamification for Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01790614 ; Games4NLP: Games and Gamification for Natural Language Processing , May 2018, Miyazaki, Japan (2018)
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Categorial Proof Nets and Dependency Locality: A New Metric for Linguistic Complexity
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In: Symposium on Logic and Algorithms in Computational Linguistics ; LACompLing: Logic and Algorithms in Computational Linguistics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01916104 ; LACompLing: Logic and Algorithms in Computational Linguistics, Aug 2018, Stockholm, Sweden. pp.73-86 ; http://staff.math.su.se/rloukanova/LACompLing2018-web/ (2018)
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L'approche logique des grammaires catégorielles : une syntaxe tournée vers la sémantique
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In: ISSN: 0182-5887 ; Verbum (Presses Universitaires de Nancy) ; https://hal.archives-ouvertes.fr/hal-02093509 ; Verbum (Presses Universitaires de Nancy), Université de Nancy II, 2018, XL (2), pp.237-267 (2018)
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SIFR Annotator: Ontology-Based Semantic Annotation of French Biomedical Text and Clinical Notes
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01934127 ; BMC Bioinformatics, BioMed Central, 2018, 19 (1), pp.405-431. ⟨10.1186/s12859-018-2429-2⟩ (2018)
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An Empirical Study for a Machine Aided Translation of French Prepositions 'à', 'de' and 'en' into English
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In: 8th Language and Technology Conference ; LTC: Language and Technology Conference ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01645154 ; LTC: Language and Technology Conference, Nov 2017, Poznan, Poland ; http://ltc.amu.edu.pl/a2017/ (2017)
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Ontolex JeuxDeMots and Its Alignment to the Linguistic Linked Open Data Cloud
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In: 16th International Semantic Web Conference ; ISWC: International Semantic Web Conference ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01615473 ; ISWC: International Semantic Web Conference, Oct 2017, Vienne, Austria. pp.678-693, ⟨10.1007/978-3-319-68288-4_40⟩ ; https://iswc2017.semanticweb.org (2017)
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FrenchSentiClass : an Automated System for French Sentiment Classification ; FrenchSentiClass : un Système Automatisé pour la Classification de Sentiments en Français
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In: Actes de l’atelier DEFT de la conférence TALN 2017 ; DEFT: Défi Fouille de Texte ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411 ; DEFT: Défi Fouille de Texte, Jun 2017, Orléans, France (2017)
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Abstract:
National audience ; This paper describes the system we used on the tasks of the text mining challenge (DEFT 2017). This thirteenth edition of this challenge concerned the analysis of opinions and figurative language in French tweets. Three tasks have been proposed : (i) the first one concerns the classification of non-figurative tweets according to their polarity ; (ii) the second one concerns the identification of figurative language, while (iii) the third one concerns the classification of figurative and non-figurative tweets according to their polarity. We proposed an automated system based on Support Vector Machines (SVM). The system automatically chooses on each step the best preprocessing, syntactic features and sentiment lexicons by cross validation on the training set. Furthermore, it performs an evaluation of feature subset selection and a tuning SVM complexity parameter. Therefore, this system can significantly reduce the time necessary to explore the data and choose the best feature representation. ; Ce papier décrit le système FrenchSentiClass que nous avons utilisé pour les tâches du défi de fouilles de texte (DEFT 2017). Cette treizième édition du défi a porté sur l'analyse de l'opinion et du langage figuratif dans des tweets rédigés en Français. Le défi propose trois tâches : (i) la première concerne la classification des tweets non figuratifs selon leur polarité ; (ii) la deuxième concerne l'identification du langage figuratif et (iii) la troisième concerne la classification des tweets figuratifs et non figuratifs selon leur polarité. Nous avons proposé un système automatisé basé sur les Machines à Vecteurs de Support (SVM). Le système choisit automatiquement à chaque niveau les meilleurs prétraitements, descripteurs syntaxiques et lexiques de sentiments en validation croisée sur l'ensemble d'apprentissage. Il effectue aussi une évaluation de l'apport de la sélection d'attributs et un tuning du paramètre de complexité du modèle SVM. Par conséquent, ce système permet de réduire considérablement le temps d'exploration des données et du choix de la meilleur représentation de descripteurs. ABSTRACT FrenchSentiClass : an Automated System for French Sentiment Classification This paper describes the system we used on the tasks of the text mining challenge (DEFT 2017). This thirteenth edition of this challenge concerned the analysis of opinions and figurative language in French tweets. Three tasks have been proposed : (i) the first one concerns the classification of non-figurative tweets according to their polarity ; (ii) the second one concerns the identification of figurative language, while (iii) the third one concerns the classification of figurative and non-figurative tweets according to their polarity. We proposed an automated system based on Support Vector Machines (SVM). The system automatically chooses on each step the best preprocessing, syntactic features and sentiment lexicons by cross validation on the training set. Furthermore, it performs an evaluation of feature subset selection and a tuning SVM complexity parameter. Therefore, this system can significantly reduce the time necessary to explore the data and choose the best feature representation. MOTS-CLÉS : Analyse d'opinions, détection de polarité, langage figuratif.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Analyse d’opinions; Détection de polarité; Figurative language; Langage figuratif; Opinion analysis; Polarity detection
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URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411 https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411/file/deft2017-frenchsenticlassEquipe4.pdf https://hal-lirmm.ccsd.cnrs.fr/lirmm-01563411/document
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Collecting Crowd-Sourced Lexical Coercions for Compositional Semantic Analysis
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In: 14th International Workshop on Logic and Engineering of Natural Language Semantics @ JSAI International Symposia on AI (isAI2017) ; LENLS: Logic and Engineering of Natural Language Semantics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01916195 ; LENLS: Logic and Engineering of Natural Language Semantics, Nov 2017, Tokyo, Japan ; http://www.is.ocha.ac.jp/~bekki/lenls/lenls14/index.html (2017)
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Ambiguss, a game for building a Sense Annotated Corpus for French
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In: 12th International Conference on Computational Semantics ; IWCS: International Conference on Computational Semantics ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01763434 ; IWCS: International Conference on Computational Semantics, Sep 2017, Montpellier, France ; https://www.lirmm.fr/iwcs2017/ (2017)
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Radiological Text Simplification Using a General Knowledge Base
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In: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part I ; 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-03108571 ; 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), Apr 2017, Budapest, Hungary. pp.617-627, ⟨10.1007/978-3-319-77116-8_46⟩ (2017)
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If mice were reptiles, then reptiles could be mammals or How to detect errors in the JeuxDeMots lexical network?
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In: 11th International Conference on Recent Advances in Natural Language Processing ; RANLP: Recent Advances in Natural Language Processing ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01763466 ; RANLP: Recent Advances in Natural Language Processing, Sep 2017, Varna, Bulgaria ; http://lml.bas.bg/ranlp2017/ (2017)
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Construire un lexique de sentiments par crowdsourcing et propagation
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In: 23e Conférence sur le Traitement Automatique des Langues Naturelles ; TALN: Traitement Automatique des Langues Naturelles ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01382273 ; TALN: Traitement Automatique des Langues Naturelles, Jul 2016, Paris, France ; https://jep-taln2016.limsi.fr/actes/index.php?lang=fr (2016)
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Discovering of new Spatial Entities and Relations from SMS ; Découverte de nouvelles entités et relations spatiales à partir d’un corpus de SMS
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In: 23e Conférence sur le Traitement Automatique des Langues Naturelles ; TALN: Traitement Automatique des Langues Naturelles ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01944710 ; TALN: Traitement Automatique des Langues Naturelles, Jul 2016, Paris, France ; https://jep-taln2016.limsi.fr/actes/index.php?lang=fr (2016)
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Mixing Crowdsourcing and Graph Propagation to Build a Sentiment Lexicon ; Mixing Crowdsourcing and Graph Propagation to Build a Sentiment Lexicon: Feelings are contagious
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In: 21st International Conference on Applications of Natural Language to Information Systems ; NLDB: Natural Language Processing and Information Systems ; https://hal-lirmm.ccsd.cnrs.fr/lirmm-01471649 ; NLDB: Natural Language Processing and Information Systems, Jun 2016, Manchester, United Kingdom. pp.258-266, ⟨10.1007/978-3-319-41754-7_23⟩ ; http://www.salford.ac.uk/conferencing-at-salford/conference-management/past-conferences/nldb-conference (2016)
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