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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 – 6 of 6 1 SemEval 2021 Task 12: Learning with Disagreement ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chamberlain, Jon; Dumitrache, Anca. - : Underline Science Inc., 2021 BASE Show details 2 SemEval-2021 Task 12: Learning with Disagreements Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021 BASE Show details 3 Making the Most of Crowd Information: Learning and Evaluation in AI tasks with Disagreements. Uma., Alexandra Nnemamaka.. - : Queen Mary University of London., 2021 BASE Show details 4 We Need to Consider Disagreement in Evaluation Basile, Valerio; Fell, Michael; Fornaciari, Tommaso. - : Association for Computational Linguistics, 2021. : country:USA, 2021. : place:Stroudsburg, PA, 2021 BASE Show details 5 A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation Paun, Silviu; Uma, Alexandra; Poesio, Massimo. - : Association for Computational Linguistics, 2019 BASE Show details 6 A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation Poesio, Massimo; Chamberlain, Jon; Paun, Silviu; Yu, Juntao; Uma, Alexandra; Kruschwitz, Udo. - : Association for Computational Linguistics, 2019 Abstract: We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented. Keyword: 020 Bibliotheks- und Informationswissenschaft; ddc:020 URL: https://epub.uni-regensburg.de/43420/1/N19-1176.pdf https://www.aclweb.org/anthology/N19-1176 https://epub.uni-regensburg.de/43420/ BASE Hide details

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