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aclweb.org
Danqi Chen et Christopher Manning, « A Fast and Accurate Dependency Parser using Neural Networks », Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (DOI10.3115/v1/d14-1082, lire en ligne)
(en) Daniel Andor, Chris Alberti, David Weiss et Aliaksei Severyn, « Globally Normalized Transition-Based Neural Networks », Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, (DOI10.18653/v1/p16-1231, lire en ligne)
(en) Miguel Ballesteros, Chris Dyer et Noah A. Smith, « Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs », Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, (DOI10.18653/v1/d15-1041, lire en ligne)
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(en) James Henderson, « Discriminative training of a neural network statistical parser », Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, , p. 95 (DOI10.3115/1218955.1218968, lire en ligne, consulté le )
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(en) Koby Crammer et Yoram Singer, « Ultraconservative online algorithms for multiclass problems », The Journal of Machine Learning Research, vol. 3, , p. 951–991 (ISSN1532-4435, DOI10.1162/jmlr.2003.3.4-5.951, lire en ligne)
(en) S. Abney, S. Flickenger, C. Gdaniec et C. Grishman, « Procedure for quantitatively comparing the syntactic coverage of English grammars », Proceedings of the workshop on Speech and Natural Language, Association for Computational Linguistics, , p. 306–311 (DOI10.3115/112405.112467, lire en ligne)
(en) Sabine Buchholz et Erwin Marsi, « CoNLL-X shared task on multilingual dependency parsing », Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), Association for Computational Linguistics, , p. 149–164 (DOI10.3115/1596276.1596305, lire en ligne, consulté le )
arxiv.org
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(en) Dan Klein et Christopher D. Manning, « Accurate unlexicalized parsing », Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, , p. 423–430 (DOI10.3115/1075096.1075150, lire en ligne)
(en) Michael Collins et Nigel Duffy, « New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron », Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, , p. 263–270 (DOI10.3115/1073083.1073128, lire en ligne)
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(en) Ryan McDonald, Fernando Pereira, Kiril Ribarov et Jan Hajič, « Non-projective dependency parsing using spanning tree algorithms », Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics, , p. 523–530 (DOI10.3115/1220575.1220641, lire en ligne, consulté le )
(en) Jinho D. Choi, Joel Tetreault et Amanda Stent, « It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool », Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, vol. 1, , p. 387–396 (DOI10.3115/v1/P15-1038, lire en ligne)
(en) Taku Kudo et Yuji Matsumoto, « Japanese dependency structure analysis based on support vector machines », Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, Association for Computational Linguistics, , p. 18–25 (DOI10.3115/1117794.1117797, lire en ligne, consulté le )
(en) Joakim Nivre et Jens Nilsson, « Pseudo-projective dependency parsing », Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, , p. 99–106 (DOI10.3115/1219840.1219853, lire en ligne, consulté le )
Danqi Chen et Christopher Manning, « A Fast and Accurate Dependency Parser using Neural Networks », Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (DOI10.3115/v1/d14-1082, lire en ligne)
(en) James Henderson, « Inducing history representations for broad coverage statistical parsing », Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Association for Computational Linguistics, , p. 24–31 (DOI10.3115/1073445.1073459, lire en ligne, consulté le )
(en) James Henderson, « Discriminative training of a neural network statistical parser », Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, , p. 95 (DOI10.3115/1218955.1218968, lire en ligne, consulté le )
(en) Daniel Andor, Chris Alberti, David Weiss et Aliaksei Severyn, « Globally Normalized Transition-Based Neural Networks », Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, (DOI10.18653/v1/p16-1231, lire en ligne)
(en) Miguel Ballesteros, Chris Dyer et Noah A. Smith, « Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs », Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, (DOI10.18653/v1/d15-1041, lire en ligne)
(en) Jason M. Eisner, « Three new probabilistic models for dependency parsing: an exploration », Proceedings of the 16th conference on Computational linguistics, Association for Computational Linguistics, , p. 340–345 (DOI10.3115/992628.992688, lire en ligne)
(en) Carlos Gómez-Rodríguez, John Carroll et David Weir, « Dependency parsing schemata and mildly non-projective dependency parsing », Computational Linguistics, vol. 37, no 3, , p. 541–586 (ISSN0891-2017, DOI10.1162/COLI_a_00060, lire en ligne)
(en) Koby Crammer et Yoram Singer, « Ultraconservative online algorithms for multiclass problems », The Journal of Machine Learning Research, vol. 3, , p. 951–991 (ISSN1532-4435, DOI10.1162/jmlr.2003.3.4-5.951, lire en ligne)
(en) Wenzhe Pei, Tao Ge et Baobao Chang, « An Effective Neural Network Model for Graph-based Dependency Parsing », Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, vol. 1, , p. 313–322 (DOI10.3115/v1/P15-1031, lire en ligne)
(en) S. Abney, S. Flickenger, C. Gdaniec et C. Grishman, « Procedure for quantitatively comparing the syntactic coverage of English grammars », Proceedings of the workshop on Speech and Natural Language, Association for Computational Linguistics, , p. 306–311 (DOI10.3115/112405.112467, lire en ligne)
(en) Sabine Buchholz et Erwin Marsi, « CoNLL-X shared task on multilingual dependency parsing », Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), Association for Computational Linguistics, , p. 149–164 (DOI10.3115/1596276.1596305, lire en ligne, consulté le )
(en) Carlos Gómez-Rodríguez, John Carroll et David Weir, « Dependency parsing schemata and mildly non-projective dependency parsing », Computational Linguistics, vol. 37, no 3, , p. 541–586 (ISSN0891-2017, DOI10.1162/COLI_a_00060, lire en ligne)
(en) Koby Crammer et Yoram Singer, « Ultraconservative online algorithms for multiclass problems », The Journal of Machine Learning Research, vol. 3, , p. 951–991 (ISSN1532-4435, DOI10.1162/jmlr.2003.3.4-5.951, lire en ligne)
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(en) Sabine Buchholz et Erwin Marsi, « CoNLL-X Shared Task on Multilingual Dependency Parsing », International Journal of Web Engineering and Technology - IJWET, (DOI10.3115/1596276.1596305, lire en ligne, consulté le )
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(en) Terry Koo et Michael Collins, « Efficient Third-Order Dependency Parsers. », Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, , p. 1–11 (lire en ligne)
(en) Xuezhe Ma et Hai Zhao, « Fourth-Order Dependency Parsing », Proceedings of COLING 2012, , p. 785–796 (lire en ligne)
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semanticscholar.org
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uni-saarland.de
aclanthology.coli.uni-saarland.de
(en) Slav Petrov, Leon Barrett, Romain Thibaux et Dan Klein, « Learning Accurate, Compact, and Interpretable Tree Annotation », Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, (lire en ligne)
(en) Tianze Shi, Felix G. Wu, Xilun Chen et Yao Cheng, « Combining Global Models for Parsing Universal Dependencies », Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics, , p. 31–39 (DOI10.18653/v1/K17-3003, lire en ligne)
(en) Anders Björkelund, Agnieszka Falenska, Xiang Yu et Jonas Kuhn, « IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks », Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Association for Computational Linguistics, , p. 40–51 (DOI10.18653/v1/K17-3004, lire en ligne)
(en) Joakim Nivre et Ryan McDonald, « Integrating Graph-Based and Transition-Based Dependency Parsers », Proceedings of ACL-08: HLT, (lire en ligne)
(en) Jinho D. Choi, Joel Tetreault et Amanda Stent, « It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool », Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, vol. 1, , p. 387–396 (DOI10.3115/v1/P15-1038, lire en ligne)
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(en) Hao Zhang et Ryan McDonald, « Generalized Higher-Order Dependency Parsing with Cube Pruning », Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, (lire en ligne)
(en) Keith Hall, « K-best Spanning Tree Parsing », Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, (lire en ligne)
(en) Emily Pitler, Sampath Kannan et Mitchell Marcus, « Dynamic Programming for Higher Order Parsing of Gap-Minding Trees », Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, (lire en ligne)
(en) Emily Pitler, Sampath Kannan et Mitchell Marcus, « Finding Optimal 1-Endpoint-Crossing Trees », Transactions of the Association of Computational Linguistics, vol. 1, (lire en ligne)
(en) Emily Pitler, « A Crossing-Sensitive Third-Order Factorization for Dependency Parsing », Transactions of the Association of Computational Linguistics, vol. 2, no 1, (lire en ligne)
