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Multiple Tasks Integration ; Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics ; EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03601585 ; EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kyiv, Ukraine (2021)
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Annotation manuelle des émotions dans des textes écrits avec la plateforme Glozz. ; Annotation manuelle des émotions dans des textes écrits avec la plateforme Glozz.: Guide d'annotation
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In: https://hal.archives-ouvertes.fr/hal-03263194 ; [Rapport de recherche] MoDyCo; Université Paris Nanterre. 2021 (2021)
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Atténuer les erreurs de numérisation dans la reconnaissance d'entités nommées pour les documents historiques
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In: Conférence en Recherche d'Informations et Applications (CORIA 2021) ; https://hal.archives-ouvertes.fr/hal-03320332 ; Conférence en Recherche d'Informations et Applications (CORIA 2021), ARIA : Association Francophone de Recherche d’Information (RI) et Applications, Apr 2021, Grenoble (virtuel), France. pp.1 - 7 ; http://coria.asso-aria.org/2021/articles/mini_24/main.pdf (2021)
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Prosodic Boundary Prediction Model for Vietnamese Text-To-Speech
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In: Proc. Interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03329116 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.3885-3889, ⟨10.21437/interspeech.2021-125⟩ (2021)
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Texte électronique enrichi par lemmatisation et étiquetage morphosyntaxique, portion de La Mort du roi Arthur , http://www.atilf.fr/dmf/MortArthur/
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In: https://hal.archives-ouvertes.fr/hal-03426756 ; 2021 (2021)
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From text saliency to linguistic objects: learning linguistic interpretable markers with a multi-channels convolutional architecture
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In: https://hal.archives-ouvertes.fr/hal-03142170 ; 2021 (2021)
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Artificial Text Detection via Examining the Topology of Attention Maps
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In: ACL Anthology ; Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03456191 ; Empirical Methods in Natural Language Processing, ACL (Association for Computational Linguistics), Nov 2021, Punta Cana, Dominican Republic (2021)
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On the Role of Low-Level Linguistic Tasks for Reading Time Prediction
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43) ; 43rd Annual Meeting of the Cognitive Science Society ; https://hal.archives-ouvertes.fr/hal-03303689 ; 43rd Annual Meeting of the Cognitive Science Society, Jul 2021, Vienna, Austria. pp.452 ; https://cognitivesciencesociety.org/cogsci-2021/ (2021)
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Grapholinguistics in the 21st Century - 2020. Part II
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In: G21C 2020 : Grapholinguistics in the 21st Century ; https://hal.archives-ouvertes.fr/hal-03161397 ; G21C 2020 : Grapholinguistics in the 21st Century, Jun 2020, Paris, France. 5, Fluxus Editions, 2021, Grapholinguistics and Its Applications, 9782957054978. ⟨10.36824/2020-graf2⟩ ; http://www.fluxus-editions.fr/gla5.php (2021)
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First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
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In: https://hal.inria.fr/hal-03161685 ; 2021 (2021)
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Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
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In: https://hal.inria.fr/hal-03161677 ; 2021 (2021)
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Collecting and annotating corpora for three under-resourced languages of France: Methodological issues
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In: ISSN: 1934-5275 ; EISSN: 1934-5275 ; Language Documentation & Conservation ; https://hal.archives-ouvertes.fr/hal-03273196 ; Language Documentation & Conservation, University of Hawaiʻi Press 2021, 15, pp.316-357 ; http://hdl.handle.net/10125/74645 (2021)
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Grapholinguistics in the 21st Century - 2020 ; Grapholinguistics in the 21st Century - 2020: Part I
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In: G21C 2020 : Grapholinguistics in the 21st Century ; https://hal.archives-ouvertes.fr/hal-03161395 ; Yannis Haralambous. G21C 2020 : Grapholinguistics in the 21st Century, Jun 2020, Paris, France. 4, Fluxus Editions, 2021, Grapholinguistics and Its Applications, 9782957054961. ⟨10.36824/2020-graf1⟩ ; http://www.fluxus-editions.fr/grafematik2020-proceedingsI.pdf (2021)
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Playing With Unicorns: AI Dungeon and Citizen NLP
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In: Digital Humanities Quarterly, vol 14, iss 4 (2021)
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Contextualized, Metadata-Empowered, Coarse-to-Fine Weakly-Supervised Text Classification
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Abstract:
Text classification plays a fundamental role in transforming unstructured text data to structured knowledge. State-of-the-art text classification techniques rely on heavy domain-specific annotations to build massive machine(deep) learning models. Although these deep learning models exhibit superior performance, the lack of training data and expensive human effort in the manual annotation is a key bottleneck that forbids them from being adopted in many practical scenarios. To address this bottleneck, our research exploits the data and develops a family of data-driven text classification frameworks with minimal supervision, for e.g. class names, a few label-indicative seed words per class.The massive volume of text data and complexity of natural language pose significant challenges to categorizing the text corpus without human annotations. For instance, the user- provided seed words can have multiple interpretations depending on the context, and their respective user-intended interpretation has to be identified for accurate classification. Moreover, metadata information like author, year, and location is widely available in addition to the text data, and it could serve as a strong, complementary source of supervision. However, leveraging metadata is challenging because (1) metadata is multi-typed, therefore it requires systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others. And also, the label set is typically assumed to be fixed in traditional text classification problems. However, in many real-world applications, new classes especially more fine-grained ones will be introduced as the data volume increases. The goal of our research is to create general data-driven methods that transform real-world text data into structured categories of human knowledge with minimal human effort.This thesis outlines a family of weakly supervised text classification approaches, which upon combining can automatically categorize huge text corpus into coarse and fine-grained classes, with just label hierarchy and a few label-indicative seed words as supervision. Specifically, it first leverages contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of a seed word, and thus result- ing in contextualized weak supervision. Then, to leverage metadata, it organizes the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata. Finally, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. We have performed extensive experiments on real-world datasets from different domains. The results demonstrate significant advantages of using contextualized weak supervision and leveraging metadata, and superior performance over baselines.
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Keyword:
Computer science; machine learning; natural language processing; text classification; weakly supervised learning
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URL: https://escholarship.org/uc/item/7hs3t90c
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Automatic simplification of technical and specialized texts ; Simplification automatique de textes techniques et spécialisés
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In: https://hal.archives-ouvertes.fr/tel-03343769 ; Informatique et langage [cs.CL]. Université de Lille, 2021. Français. ⟨NNT : 2021LILUH007⟩ (2021)
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Automatic text simplification of specialized and technical texts ; Simplification automatique de textes techniques et spécialisés
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In: https://hal.archives-ouvertes.fr/tel-03343769 ; Informatique et langage [cs.CL]. Université de Lille, 2021. Français (2021)
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Models of diachronic semantic change using word embeddings ; Modèles diachroniques à base de plongements de mot pour l'analyse du changement sémantique
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In: https://tel.archives-ouvertes.fr/tel-03199801 ; Document and Text Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG006⟩ (2021)
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Hate speech and offensive language detection using transfer learning approaches ; Détection du discours de haine et du langage offensant utilisant des approches de Transfer Learning
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In: https://tel.archives-ouvertes.fr/tel-03276023 ; Document and Text Processing. Institut Polytechnique de Paris, 2021. English. ⟨NNT : 2021IPPAS007⟩ (2021)
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Extraction and normalization of simple and structured entities in medical documents ; Extraction et normalisation d'entités simples et structurées dans les documents médicaux
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In: https://hal.archives-ouvertes.fr/tel-03624928 ; Document and Text Processing. Sorbonne Université, 2021. English (2021)
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