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1
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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2
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios
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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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)
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6
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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7
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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8
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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9
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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10
SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations
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)
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11
Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian
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)
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12
Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations
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)
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13
Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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14
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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15
Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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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
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17
Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations
In: International Workshop on Computational Linguistics for Uralic languages. - Helsinki, ISBN: (2018)
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18
A System for Multilingual Dependency Parsing based on Bidirectional LSTM Feature Representations
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)
Abstract: International audience ; In this paper, we present our multilingual dependency parser developed for the CoNLL 2017 UD Shared Task dealing with " Multilingual Parsing from Raw Text to Universal Dependencies " 1. Our parser extends the monolingual BIST-parser as a multi-source multilingual trainable parser. Thanks to multilingual word embeddings and one hot encodings for languages, our system can use both monolingual and multi-source training. We trained 69 monolingual language models and 13 multilingual models for the shared task. Our multilingual approach making use of different resources yield better results than the monolingual approach for 11 languages. Our system ranked 5 th and achieved 70.93 overall LAS score over the 81 test corpora (macro-averaged LAS F1 score).
Keyword: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; Multilingualism; Parsing
URL: https://hal.archives-ouvertes.fr/hal-01722370/file/K17-3006.pdf
https://hal.archives-ouvertes.fr/hal-01722370/document
https://hal.archives-ouvertes.fr/hal-01722370
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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
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