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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 19 of 19 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios Lim, KyungTae; Lee, Jay Yoon; Carbonell, Jaime; Poibeau, Thierry 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) 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. 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 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 BASE Hide details 5 Bootstrap methods for multi-task dependency parsing in low-resource conditions ; Méthodes d’amorçage pour l’analyse en dépendances de langues peu dotées Lim, Kyungtae. - : HAL CCSD, 2020 In: https://tel.archives-ouvertes.fr/tel-03477961 ; Linguistics. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLE027⟩ (2020) BASE Show details 6 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 7 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 8 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 9 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 10 SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations Lim, Kyungtae; Park, Cheoneum; Lee, Changki... In: Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies ; https://hal.archives-ouvertes.fr/hal-02977455 ; Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Oct 2018, Bruxelles, Belgium. pp.143-152, ⟨10.18653/v1/K18-2014⟩ ; https://www.conll.org/2018/ (2018) BASE Show details 11 Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian Lim, KyungTae; Partanen, Niko; Poibeau, Thierry In: LREC 2018 Proceedings ; Language Resource and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01856178 ; Language Resource and Evaluation Conference, ELRA, May 2018, Miyazaki, Japan ; http://www.lrec-conf.org/proceedings/lrec2018/pdf/600.pdf (2018) BASE Show details 12 Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations Partanen, Niko; Lim, KyungTae; Rießler, Michael... In: International Workshop on Computational Linguistics for Uralic Languages ; https://hal.archives-ouvertes.fr/hal-01722243 ; International Workshop on Computational Linguistics for Uralic Languages, Jan 2018, Helsinki, Finland. pp.1 - 17 ; aclweb.org/anthology/W18-0200 (2018) BASE Show details 13 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details 14 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 15 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 16 CoNLL 2018 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 17 Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations Lim, Kyungtae; Poibeau, Thierry; Rießler, Michael... In: International Workshop on Computational Linguistics for Uralic languages. - Helsinki, ISBN: (2018) BASE Show details 18 A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations Lim, KyungTae; Poibeau, Thierry In: Computational Natural Language Learning (CoNLL) ; https://hal.archives-ouvertes.fr/hal-01722370 ; Computational Natural Language Learning (CoNLL), ACL, Aug 2017, Vancouver, Canada. pp.63 - 70 ; https://aclanthology.coli.uni-saarland.de/papers/K17-3006/k17-3006 (2017) BASE Show details 19 CoNLL 2017 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017 BASE Show details

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