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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; Kruschwitz, Udo; Poesio, Massimo. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019 Abstract: *Introduction* Phrase Detectives Corpus Version 2 was developed by the School of Computer Science and Electronic Engineering at the University of Essex and consists of approximately 407,000 tokens across 537 documents anaphorically-annotated by the Phrase Detectives Game, an online interactive "game-with-a-purpose" (GWAP) designed to collect data about English anaphoric coreference. This release constitutes a new version of the Phrase Detectives Corpus (LDC2017T08) that adds significantly more annotated tokens to the data set and supplies for each markable a substantial number of judgments expressed by the players and a silver label annotation based on the probabilistic aggregation method for anaphoric information. GWAPs for creating language resources are growing. In general, they employ non-monetary incentives, such as entertainment, to motivate participation and can be successful for large-scale persistent annotation efforts. Two projects that collect linguistic resources via Phrase Detectives and other similar language-oriented GWAPs are DALI (Disagreements and Language Interpretation), led by Queen Mary University of London and the University of Essex, and the LDC NIEUW (Novel Incentives and Workflows in Linguistic Data Annotation) project through its game site Lingo Boingo, in collaboration with Queen Mary University, the University of Essex and other partners. *Data* The documents in the corpus are taken from Wikipedia articles and from narrative text in Project Gutenberg. The annotation is a simplified form of the coding scheme used in The ARRAU Corpus of Anaphoric Information (LDC2013T22). Players were asked to classify markables as referring or non-referring. Referring noun phrases could be classified either as discourse-new or discourse-old (referring to the same entity as a previous mention). Two types of non-referring expressions are identified: expletives and predicative NPs (called 'properties'). Discourse-old markables include so-called split antecedent plurals, as in Mary met John. They had dinner together. All player judgments are stored in MAS-XML format; they average 20 judgments per markable, up to 90 judgments in one case. A silver label extracted from those judgments using the MPA probabilistic annotation method (Paun et. al, 2018) is also provided. Wikipedia articles are presented as html, and all other source files are presented as plain text. All text is encoded as UTF-8. Annotations are released in three formats: (1) MAS-XML (the format in the first release), (2) a CONLL-style format based on the CoNLL 2011 and 2012 shared tasks on coreference and (3) CRAC 2018 format. *Samples* Please view the following samples: * Source * CoNLL * CRAC * MAS-XML *Updates* None at this time. URL: https://catalog.ldc.upenn.edu/LDC2019T10 BASE Hide 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. - : Universität Regensburg, 2019 BASE Show 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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