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1
SemEval 2021 Task 12: Learning with Disagreement ...
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2
SemEval-2021 Task 12: Learning with Disagreements
Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021
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3
Phrase Detectives Corpus Version 2
Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019
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4
Phrase Detectives Corpus Version 2 ...
Chamberlain, Jon; Paun, Silviu; Yu, Juntao. - : Linguistic Data Consortium, 2019
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5
Crowdsourcing and Aggregating Nested Markable Annotations ...
Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Universität Regensburg, 2019
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6
Crowdsourcing and Aggregating Nested Markable Annotations
Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019
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7
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Paun, Silviu; Uma, Alexandra; Poesio, Massimo. - : Association for Computational Linguistics, 2019
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8
Crowdsourcing and Aggregating Nested Markable Annotations
Poesio, Massimo; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019
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9
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Poesio, Massimo; Chamberlain, Jon; Paun, Silviu. - : Association for Computational Linguistics, 2019
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10
Exploring Language Style in Chatbots to Increase Perceived Product Value and User Engagement
Elsholz, Ela; Chamberlain, Jon; Kruschwitz, Udo. - : ACM (Association for Computing Machinery), 2019
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11
A Probabilistic Annotation Model for Crowdsourcing Coreference
Kruschwitz, Udo; Chamberlain, Jon; Yu, Juntao. - : Association for Computational Linguistics, 2018
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12
Phrase Detectives Corpus
Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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13
Phrase Detectives Corpus ...
Chamberlain, Jon; Poesio, Massimo; Kruschwitz, Udo. - : Linguistic Data Consortium, 2017
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14
Markup Infrastructure for the Anaphoric Bank: Supporting Web Collaboration
Abstract: Modern NLP systems rely either on unsupervised methods, or on data created as part of governmental initiatives such as MUC, ACE, or GALE. The data created in these efforts tend to be annotated according to task-specific schemes. The Anaphoric Bank is an attempt to create large quantities of data annotated with anaphoric information according to a general purpose and linguistically motivated scheme. We do this by pooling smaller amounts of data annotated according to rich schemes that are by and large compatible, and by taking advantage of Web collaboration. In this chapter we discuss the markup infrastructure that underpins the two modalities of Web collaboration in the project: expert annotation and game-based annotation.
Keyword: 020 Bibliotheks- und Informationswissenschaft; ddc:020
URL: https://epub.uni-regensburg.de/40373/
https://doi.org/10.1007/978-3-642-22613-7_10
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