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Hits 101 – 120 of 5.767

101	Stratégie Multitâche pour la Classification Multiclasse
	Akhmouch, Houssam; Bouanani, Hamza; Dias, Gaël...
	In: à paraître : Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale ; Traitement Automatique des Langues Naturelles (TALN 2021) ; https://hal.archives-ouvertes.fr/hal-03265870 ; Traitement Automatique des Langues Naturelles (TALN 2021), 2021, Lille, France. pp.227-236 ; https://talnrecital2021.inria.fr/articles-acceptes/ (2021)
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102	Extraction automatique de relations sémantiques d’hyperonymie et d’hyponymie dans un corpus métier
	Gosset, Camille; Billami, Mokhtar Boumedyen; Lafourcade, Mathieu...
	In: Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale ; Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03265877 ; Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.162-170 (2021)
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103	Team LIA/LS2N at BioCreative VII LitCovid Track: Multi-label Document Classification for COVID-19 Literature using Keyword Based Enhancement and Few-Shot Learning
	Labrak, Yanis; Dufour, Richard
	In: BioCreative VII Challenge Evaluation Workshop ; https://hal.archives-ouvertes.fr/hal-03426326 ; BioCreative VII Challenge Evaluation Workshop, Nov 2021, Virtual Conference, United States (2021)
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104	Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers’ Voice Quality
	Chanclu, Anaïs; Amor, Imen Ben; Gendrot, Cédric...
	In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03334492 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.1015-1018, ⟨10.21437/Interspeech.2021-1765⟩ (2021)
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105	Contextualized, Metadata-Empowered, Coarse-to-Fine Weakly-Supervised Text Classification
	Mekala, Dheeraj. - : eScholarship, University of California, 2021
	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.
	Keyword: Computer science; machine learning; natural language processing; text classification; weakly supervised learning
	URL: https://escholarship.org/uc/item/7hs3t90c
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106	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
	Mozafari, Marzieh. - : HAL CCSD, 2021
	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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107	Contextualization of Web contents through semantic enrichment from linked open data ; Contextualisation des contenus Web par l'enrichissement sémantique à partir de données
	Kumar, Amit. - : HAL CCSD, 2021
	In: https://tel.archives-ouvertes.fr/tel-03561788 ; Databases [cs.DB]. Normandie Université, 2021. English. ⟨NNT : 2021NORMC243⟩ (2021)
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108	The pronoun in Rif Berber (from Senhaja to Iznasen)
	Lafkioui, Mena B.
	In: Les études berbères à l’ère de l’institutionnalisation de tamaziγt. Mélanges en l’honneur de Salem Chaker et Abdellah Bounfour. ; https://hal.archives-ouvertes.fr/hal-02936008 ; Merolla, Daniela et al. (eds.). Les études berbères à l’ère de l’institutionnalisation de tamaziγt. Mélanges en l’honneur de Salem Chaker et Abdellah Bounfour., L’Harmattan, pp.197-225, 2021 (2021)
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109	On the Transferability of Neural Models of Morphological Analogies
	Alsaidi, Safa; Decker, Amandine; Lay, Puthineath...
	In: AIMLAI, ECML PKDD 2021: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ; https://hal.inria.fr/hal-03313591 ; AIMLAI, ECML PKDD 2021: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2021, Bilbao/Virtual, Spain ; https://2021.ecmlpkdd.org/ (2021)
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110	Gallo-Roman
	Brun-Trigaud, Guylaine
	In: Dialect classification of languages in Europe ; https://halshs.archives-ouvertes.fr/halshs-03484298 ; Aurrekoetxea, Gotzon; Ensunza Aldamizetxebarria, Ariane; van de Velde, Hans. Dialect classification of languages in Europe, Language Science Press, In press (2021)
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111	Simulating reading mistakes for child speech Transformer-based phone recognition
	Gelin, Lucile; Pellegrini, Thomas; Pinquier, Julien...
	In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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112	Phoneme-to-Audio Alignment with Recurrent Neural Networks for Speaking and Singing Voice
	Teytaut, Yann; Roebel, Axel
	In: Proceedings of Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03552964 ; Proceedings of Interspeech 2021, International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.61-65, ⟨10.21437/interspeech.2021-1676⟩ ; https://www.interspeech2021.org/ (2021)
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113	Les classifications zoologiques d’Aristote à Linné. Approches historiques et lexicologiques
	Ben saad, Meyssa; THUAULT, Simon; Boudes, Yoan. - : HAL CCSD, 2021. : Kimé, 2021. : Editions Kimé, 2021
	In: ISSN: 1279-7243 ; Bulletin d’histoire et d’épistémologie des sciences de la vie ; https://hal.archives-ouvertes.fr/hal-03251968 ; Bulletin d’histoire et d’épistémologie des sciences de la vie , 28 (1), Kimé, 2021, 2728-3313 ; https://www.cairn.info/revue-bulletin-d-histoire-et-d-epistemologie-des-sciences-de-la-vie-2021-1.htm (2021)
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114	Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
	Trouvain, Nathan; Hinaut, Xavier
	In: https://hal.inria.fr/hal-03203374 ; 2021 (2021)
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115	Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
	Trouvain, Nathan; Hinaut, Xavier
	In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203374 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia. pp.71--82, ⟨10.1007/978-3-030-86383-8_6⟩ ; https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6 (2021)
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116	End-to-end acoustic modelling for phone recognition of young readers
	Gelin, Lucile; Daniel, Morgane; Pinquier, Julien...
	In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-03373156 ; Speech Communication, Elsevier : North-Holland, 2021, 134, pp.71-84. ⟨10.1016/j.specom.2021.08.003⟩ ; https://www.sciencedirect.com/science/article/pii/S0167639321000959?via%3Dihub (2021)
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117	Comparison of Deep Learning Approaches for Protective Behaviour Detection Under Class Imbalance from MoCap and EMG data
	Radouane, Karim; Tchechmedjiev, Andon; Xu, Binbin...
	In: ACIIW 2021 - 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos ; https://hal.archives-ouvertes.fr/hal-03523502 ; ACIIW 2021 - 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, Sep 2021, Nara, Japan. pp.01-08, ⟨10.1109/ACIIW52867.2021.9666417⟩ ; http://www.casapaganini.it/entimement/workshops/2021/Workshop2021_Home.php (2021)
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118	МЕСТО ЛИНГВИСТИЧЕСКОЙ ЭКСПЕРТИЗЫ В СИСТЕМЕ СУДЕБНЫХ ЭКСПЕРТИЗ ... : The place of linguistic examination in the forensic examination system ...
	Альфия Радиковна Сысенко. - : Закон и право, 2021
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119	К ВОПРОСУ О ТИПОЛОГИИ ТЕКСТА КАК КОГНИТИВНО-РЕЧЕВОГО ПРОИЗВЕДЕНИЯ ... : ON THE QUESTION OF TEXT TYPOLOGY AS A COGNITIVE SPEECH WORK ...
	К.З. Зулпукаров; З.М. Сабиралиева. - : Мир науки, культуры, образования, 2021
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120	К ВОПРОСУ О ЛИНГВИСТИЧЕСКОЙ ТРАКТОВКЕ МЕТАФОРЫ ... : ON THE QUESTION OF LINGUISTIC INTERPRETATION OF A METAPHOR ...
	Р.С. Ильясова. - : Мир науки, культуры, образования, 2021
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