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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 Page: 1 2 3 4 5 Hits 1 – 20 of 97 1 Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics Bruera, Andrea; Poesio, Massimo In: Front Artif Intell (2022) BASE Show details 2 Coreference Resolution for the Biomedical Domain: A Survey ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lu, Pengcheng; Poesio, Massimo. - : Underline Science Inc., 2021 BASE Show details 3 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 4 Patterns of Lexical Ambiguity in Contextualised Language Models ... Haber, Janosch; Poesio, Massimo. - : arXiv, 2021 BASE Show details 5 Patterns of Polysemy and Homonymy in Contextualised Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Haber, Janosch; Poesio, Massimo. - : Underline Science Inc., 2021 BASE Show details 6 Data Augmentation Methods for Anaphoric Zero Pronouns ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Aloraini, Abdulrahman; Poesio, Massimo. - : Underline Science Inc., 2021 BASE Show details 7 SemEval-2021 Task 12: Learning with Disagreements Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021 BASE Show details 8 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 9 Fake opinion detection: how similar are crowdsourced datasets to real data? [<Journal>] Fornaciari, Tommaso [Verfasser]; Cagnina, Leticia [Verfasser]; Rosso, Paolo [Verfasser]. DNB Subject Category Language Show details 10 Neural Coreference Resolution for Arabic ... The 28th International Conference on Computational Linguistics 2020; Aloraini, Abdulrahman; Poesio, Massimo. - : Underline Science Inc., 2020 BASE Show details 11 Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance ... The 28th International Conference on Computational Linguistics 2020; Haber, Janosch; Poesio, Massimo. - : Underline Science Inc., 2020 BASE Show details 12 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 13 Anaphoric Zero Pronoun Identification: A Multilingual Approach ... The 28th International Conference on Computational Linguistics 2020; Aloraini, Abdulrahman; Poesio, Massimo. - : Underline Science Inc., 2020 BASE Show details 14 Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data? Fornaciari, Tommaso; Cagnina, Leticia; Rosso, Paolo. - : Springer-Verlag, 2020 BASE Show details 15 Phrase Detectives Corpus Version 2 Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019 BASE Show details 16 Phrase Detectives Corpus Version 2 ... Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019 BASE Show details 17 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 18 Crowdsourcing and Aggregating Nested Markable Annotations Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 19 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 20 Crowdsourcing and Aggregating Nested Markable Annotations Poesio, Massimo; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details Page: 1 2 3 4 5

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