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GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ...
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Human-like informative conversations: Better acknowledgements using conditional mutual information ...
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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ...
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Biomedical and clinical English model packages for the Stanza Python NLP library
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In: J Am Med Inform Assoc (2021)
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Stanza: A Python Natural Language Processing Toolkit for Many Human Languages ...
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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation ...
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Finding Universal Grammatical Relations in Multilingual BERT ...
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CoQA: A Conversational Question Answering Challenge
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 249-266 (2019) (2019)
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Arc-swift: A Novel Transition System for Dependency Parsing ...
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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
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Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira; Gokirmak, Memduh; Reddy, Siva; Kettnerová, Václava; Popel, Martin; Nitisaroj, Rattima; Leung, Herman; Pyysalo, Sampo; Ginter, Filip; Missilä, Anna; dePaiva, Valeria; Martínez Alonso, Héctor; Habash, Nizar; Banerjee, Esha; Tyers, Francis; Sanguinetti, Manuela; Lertpradit, Saran; Uszkoreit, Hans; Yu, Zhuoran; Li, Josie; Sulubacak, Umut; Simi, Maria; Harris, Kim; de Marneffe, Marie-Catherine; Burchardt, Aljoscha; Straka, Milan; Kanayama, Hiroshi; Rehm, Georg; Hlavacova, Jaroslava; Attia, Mohammed; Elkahky, Ali; Kwak, Sookyoung; Kanerva, Jenna; Nedoluzhko, Anna; Marheinecke, Katrin; Stella, Antonio; Lando, Tatiana; Badmaeva, Elena; Uresova, Zdenka; Hajic, Jan; Pitler, Emily; Potthast, Martin; Cinkova, Silvie; Ojala, Stina; Strnadová, Jana; Taji, Dima; Manning, Christopher D.; Mandl, Michael; Kirchner, Jesse; Mendonca, Gustavo; Alcalde, Hector Fernandez; Schuster, Sebastian; Petrov, Slav; Zeman, Daniel; Shimada, Atsuko; Nivre, Joakim; Luotolahti, Juhani; Hajic jr., Jan; Manurung, Ruli; Macketanz, Vivien. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017
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Abstract:
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a realworld setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.
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Keyword:
dependency syntax; evaluation; parsing; Universal Dependencies
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URL: http://www.aclweb.org/anthology/K/K17/K17-3001.pdf https://doi.org/10.18653/v1/K17-3001 http://hdl.handle.net/2318/1652589
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Improving Coreference Resolution by Learning Entity-Level Distributed Representations ...
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A large annotated corpus for learning natural language inference ...
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Text to 3D Scene Generation with Rich Lexical Grounding ...
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Learning Distributed Word Representations for Natural Logic Reasoning ...
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Discovery of Deep Structure from Unlabeled Data
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In: DTIC (2014)
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Induced lexico-syntactic patterns improve information extraction from online medical forums
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