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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 – 2 of 2 1 Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017 BASE Show details 2 CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies 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 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. Keyword: dependency syntax; evaluation; parsing; Universal Dependencies 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 BASE Hide details

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