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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 – 11 of 11 1 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 2 Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution ... The 28th International Conference on Computational Linguistics 2020; Moosavi, Nafise Sadat; Paun, Silviu. - : Underline Science Inc., 2020 BASE Show details 3 Phrase Detectives Corpus Version 2 Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019 BASE Show details 4 Phrase Detectives Corpus Version 2 ... Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019 BASE Show details 5 Crowdsourcing and Aggregating Nested Markable Annotations ... Madge, Chris; Yu, Juntao; Chamberlain, Jon; Kruschwitz, Udo; Paun, Silviu; Poesio, Massimo. - : Universität Regensburg, 2019 Abstract: One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand—which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a ... Keyword: 020 Bibliotheks- und Informationswissenschaft URL: https://epub.uni-regensburg.de/id/eprint/43402 https://dx.doi.org/10.5283/epub.43402 BASE Hide details 6 Crowdsourcing and Aggregating Nested Markable Annotations Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 7 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 8 Crowdsourcing and Aggregating Nested Markable Annotations Poesio, Massimo; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 9 A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation Poesio, Massimo; Chamberlain, Jon; Paun, Silviu. - : Association for Computational Linguistics, 2019 BASE Show details 10 Comparing Bayesian Models of Annotation Paun, Silviu; Carpenter, Bob; Hovy, Dirk. - : Association for Computational Linguistics, 2018 BASE Show details 11 A Probabilistic Annotation Model for Crowdsourcing Coreference Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018 BASE Show details

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