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GeSERA: General-domain Summary Evaluation by Relevance Analysis
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In: RANLP 2021 Recent Advances in Natural Language Processing ; https://hal-cnrs.archives-ouvertes.fr/hal-03408902 ; RANLP 2021 Recent Advances in Natural Language Processing, Sep 2021, Online, Bulgaria (2021)
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Deep Learning Enhanced with Graph Knowledge for Sentiment Analysis
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In: 6th International Workshop on eXplainable SENTIment Mining and EmotioN deTection co-located with 18th Extended Semantic Web Conference 2021 (X-SENTIMENT 2021) ; https://hal.archives-ouvertes.fr/hal-03558933 ; 6th International Workshop on eXplainable SENTIment Mining and EmotioN deTection co-located with 18th Extended Semantic Web Conference 2021 (X-SENTIMENT 2021), Jun 2021, Hersonissos, Greece. pp.74-86 ; http://ceur-ws.org/Vol-2918/ (2021)
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Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing
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In: 17th International Conference on Parsing Technologies (IWPT 2021) ; https://hal.archives-ouvertes.fr/hal-03558605 ; 17th International Conference on Parsing Technologies (IWPT 2021), Aug 2021, Online, United States. p. 106-118 ; https://aclanthology.org/volumes/2021.iwpt-1/ (2021)
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Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing ...
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GeSERA: General-domain Summary Evaluation by Relevance Analysis ...
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Integrating Dependency Parses with Sequential Patterns to Improve Relation Extraction ; Apport des dépendances syntaxiques et des patrons séquentiels à l'extraction de relations
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In: TALN 2018 ; https://hal.inria.fr/hal-02079719 ; TALN 2018, May 2018, Rennes, France (2018)
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The balance between quantitative and qualitative literary stylistics: How the method of "motifs" can help
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In: The Grammar of Genres and styles ; https://hal.archives-ouvertes.fr/hal-03546266 ; Legallois, Charnois, Larjavaara. The Grammar of Genres and styles, De Gruyter Mouton, 2018, 9783110589689. ⟨10.1515/9783110595864-008⟩ ; https://www-degruyter-com.ezproxy.univ-paris3.fr/document/doi/10.1515/9783110595864/html (2018)
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Approaching French theatrical characters by syntactical analysis: a study with motifs and correspondence analysis
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In: The Grammar of Genres and Styles. From Discrete to Non-Discrete Units ; https://hal.archives-ouvertes.fr/hal-03482615 ; Dominique Legallois; Thierry Charnois; Meri Larjavaara. The Grammar of Genres and Styles. From Discrete to Non-Discrete Units, 320, De Gruyter Mouton, pp.118-139, 2018, Trends in Linguistics. Studies and Monographs [TiLSM], 978-3-11-058968-9. ⟨10.1515/9783110595864-006⟩ (2018)
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An Experimental Approach For Information Extraction in Multi-Party Dialogue Discourse
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In: CICLing 2018 - 19th International Conference on Computational Linguistics and Intelligent Text Processing ; https://hal.archives-ouvertes.fr/hal-01804147 ; CICLing 2018 - 19th International Conference on Computational Linguistics and Intelligent Text Processing, Mar 2018, Hanoi, Vietnam. pp.1-14 (2018)
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The Grammar of Genres and Styles : From Discrete to Non-Discrete Units
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In: https://hal.archives-ouvertes.fr/hal-03540351 ; 2018, 9783110589689 (2018)
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Linguistic features of genre and method variation in translation: A computational perspective
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In: 92 ; 117 (2018)
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Combining Syntactic and Sequential Patterns for Unsupervised Semantic Relation Extraction.
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In: https://hal.archives-ouvertes.fr/hal-01591501 ; CEUR-WS.org. Macedonia. 1881, pp.81-84, 2017, Interactions between Data Mining and Natural Language Processing 2017 (2017)
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Identifying clichés in romance novels using the "motifs" method ; Repérer les clichés dans les romans sentimentaux grâce à la méthode des « motifs »
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In: ISSN: 1146-6480 ; EISSN: 1960-6052 ; LIDIL - Revue de linguistique et de didactique des langues ; https://hal.archives-ouvertes.fr/hal-01956486 ; LIDIL - Revue de linguistique et de didactique des langues, UGA Editions, 2016 (2016)
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Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature
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In: LREC 2016 ; https://hal.archives-ouvertes.fr/hal-01360407 ; LREC 2016, May 2016, Portoroz, Slovenia (2016)
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Pattern mining and CRF for symptoms recognition in biomedical texts ; Fouille de motifs et CRF pour la reconnaissance de symptômes dans les textes biomédicaux
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In: 23e conférence sur le Traitement Automatique des Langues Naturelles (TALN’16) ; https://halshs.archives-ouvertes.fr/halshs-01727081 ; 23e conférence sur le Traitement Automatique des Langues Naturelles (TALN’16), Jul 2016, Paris, France. pp.194-206 (2016)
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Abstract:
National audience ; Pattern mining and CRF for symptoms recognition in biomedical texts. In this paper, we tackle the issue of symptoms recognition in biomedical texts. There is not much attention to this problem in the literature and it does not exist to our knowledge an annotated dataset to train a model. We propose two weakly-supervised approaches to extract these entities. The first is based on pattern mining and introduces a new constraint based on semantic similarity. The second represents the task as sequence labeling using CRF (Conditional Random Fields). We describe our experiments which show that the two approaches are complementary in terms of quantification (recall and precision). We further show that their combination significantly improves the results. ; Dans cet article, nous nous intéressons à l'extraction d'entités médicales de type symptôme dans les textes biomédicaux. Cette tâche est peu explorée dans la littérature et il n'existe pas à notre connaissance de corpus annoté pour entraîner un modèle d'apprentissage. Nous proposons deux approches faiblement supervisées pour extraire ces entités. Une première est fondée sur la fouille de motifs et introduit une nouvelle contrainte de similarité sémantique. La seconde formule la tache comme une tache d'étiquetage de séquences en utilisant les CRF (champs conditionnels aléatoires). Nous décrivons les expérimentations menées qui montrent que les deux approches sont complémentaires en termes d'évaluation quantitative (rappel et précision). Nous montrons en outre que leur combinaison améliore sensiblement les résultats.
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Keyword:
[SHS.LANGUE]Humanities and Social Sciences/Linguistics; Biomedical texts; CRF; Extraction d'information; Fouille de motifs; Information extraction; Pattern mining; Reconnaissance de symptômes; Symptoms recognition; Texte biomédicaux
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URL: https://halshs.archives-ouvertes.fr/halshs-01727081/document
https://halshs.archives-ouvertes.fr/halshs-01727081/file/taln-2016-paper-actes.pdf
https://halshs.archives-ouvertes.fr/halshs-01727081
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| 19 |
Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text
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In: 15th International Symposium on Intelligent Data Analysis ; https://halshs.archives-ouvertes.fr/halshs-01727071 ; 15th International Symposium on Intelligent Data Analysis, Oct 2016, Stockholm, Sweden (2016)
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Identification of Shell Nouns, Signals of Discourse Organisation ; Identification des noms sous-spécifiés, signaux de l’organisation discursive
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In: Proceedings of TALN 2014 (Volume 1: Long Papers) ; 21ème conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-01076760 ; 21ème conférence sur le Traitement Automatique des Langues Naturelles, Jul 2014, Marseille, France. pp.377-388 ; https://www.aclweb.org/anthology/F14-1033 (2014)
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