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Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Task ...
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Automatic enjambment detection as a new source of evidence in Spanish versification
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In: Plotting Poetry: On Mechanically Enhanced Reading ; https://hal.archives-ouvertes.fr/hal-03255481 ; Bories, Anne-Sophie; Purnelle, Gérald; Marchal, Hugues. Plotting Poetry: On Mechanically Enhanced Reading, Presses Universitaires de Liège, 2021, 978-2-87562-280-8 ; http://www.presses.uliege.be/ (2021)
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The Corpus for Idiolectal Research (CIDRE)
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In: European Association of Digital Humanities Conference (EADH 2021) ; https://hal.archives-ouvertes.fr/hal-03353520 ; European Association of Digital Humanities Conference (EADH 2021), Sep 2021, Krasnoyarsk, Russia (2021)
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Evaluating Hierarchical Clustering Methods for Corpora with Chronological Order
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In: EADH2021: Interdisciplinary Perspectives on Data. Second International Conference of the European Association for Digital Humanities ; https://hal.archives-ouvertes.fr/hal-03341803 ; EADH2021: Interdisciplinary Perspectives on Data. Second International Conference of the European Association for Digital Humanities, EADH, Sep 2021, Krasnoyarsk, Russia ; https://eadh2020-2021.org/ (2021)
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The Corpus for Idiolectal Research (CIDRE)
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In: EISSN: 2059-481X ; Journal of Open Humanities Data ; https://hal.archives-ouvertes.fr/hal-03310451 ; Journal of Open Humanities Data, Ubiquity Press, 2021, 7, pp.15. ⟨10.5334/johd.42⟩ (2021)
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Text Zoning of Theater Reviews: How Different are Journalistic from Blogger Reviews?
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In: Workshop on Natural Language Processing for Digital Humanities ; https://hal.archives-ouvertes.fr/hal-03498270 ; Workshop on Natural Language Processing for Digital Humanities, Dec 2021, Sichar, India ; https://rootroo.com/downloads/nlp4dh_proceedings_draft.pdf (2021)
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The Corpus for Idiolectal Research (CIDRE)
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In: Journal of Open Humanities Data; Vol 7 (2021); 15 ; 2059-481X (2021)
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ACCOLÉ : Annotation Collaborative d'erreurs de traduction pour COrpus aLignÉs – Nouvelles fonctionnalités
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In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT) ; https://hal.archives-ouvertes.fr/hal-03047150 ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT), 2020, Montrouge, France. pp.1-8 (2020)
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Glossary: Introduction to the Digital Humanities ; Glossaire : Introduction aux humanités numériques
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In: https://hal.archives-ouvertes.fr/hal-02410396 ; 2020 (2020)
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020)
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Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios
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In: Conference of the Association for the Advancement of Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-02895835 ; Conference of the Association for the Advancement of Artificial Intelligence, Association for the Advancement of Artificial Intelligence, Feb 2020, New York, United States ; https://aaai.org/Conferences/AAAI-20/ (2020)
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Abstract:
International audience ; Multi-view learning makes use of diverse models arising from multiple sources of input or different feature subsets for the same task. For example, a given natural language processing task can combine evidence from models arising from character, morpheme, lexical, or phrasal views. The most common strategy with multi-view learning, especially popular in the neural network community, is to unify multiple representations into one unified vector through concatenation, averaging, or pooling , and then build a single-view model on top of the unified representation. As an alternative, we examine whether building one model per view and then unifying the different models can lead to improvements, especially in low-resource scenarios. More specifically, taking inspiration from co-training methods, we propose a semi-supervised learning approach based on multi-view models through consensus promotion, and investigate whether this improves overall performance. To test the multi-view hypothesis, we use moderately low-resource scenarios for nine languages and test the performance of the joint model for part-of-speech tagging and dependency parsing. The proposed model shows significant improvements across the test cases, with average gains of −0.9 ∼ +9.3 labeled attachment score (LAS) points. We also investigate the effect of unlabeled data on the proposed model by varying the amount of training data and by using different domains of unlabeled data.
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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; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; [SHS.LANGUE]Humanities and Social Sciences/Linguistics
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URL: https://hal.archives-ouvertes.fr/hal-02895835
https://hal.archives-ouvertes.fr/hal-02895835/document
https://hal.archives-ouvertes.fr/hal-02895835/file/Cometa_AAAI20.pdf
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Lexical encoding of multiword expressions in XMG
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In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT) ; https://hal.archives-ouvertes.fr/hal-03047145 ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT), Dec 2020, Montrouge, France. pp.60-63 (2020)
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Classification des catégories grammaticales sur deux corpus longitudinaux d’enfants
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In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT) ; https://hal.archives-ouvertes.fr/hal-03047149 ; 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT), 2020, Montrouge, France. pp.23-33 (2020)
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Longform recordings : Opportunities and challenges ; Enregistrements de longue durée: Opportunités et défis
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In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain" ; https://hal.archives-ouvertes.fr/hal-03047153 ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain", Dec 2020, Montrouge / Virtual, France. pp.64-71 (2020)
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