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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; Paun, Silviu; Poesio, Massimo. - : Association for Computational Linguistics, 2018
Abstract: The availability of large scale annotated corpora for coreference is essential to the development of the field. However, creating resources at the required scale via expert annotation would be too expensive. Crowdsourcing has been proposed as an alternative; but this approach has not been widely used for coreference. This paper addresses one crucial hurdle on the way to make this possible, by introducing a new model of annotation for aggregating crowdsourced anaphoric annotations. The model is evaluated along three dimensions: the accuracy of the inferred mention pairs, the quality of the post-hoc constructed silver chains, and the viability of using the silver chains as an alternative to the expert-annotated chains in training a state of the art coreference system. The results suggest that our model can extract from crowdsourced annotations coreference chains of comparable quality to those obtained with expert annotation.
Keyword: QA75 Electronic computers. Computer science
URL: http://repository.essex.ac.uk/23421/1/Paun2018Probabilistic.pdf
https://www.aclweb.org/anthology/D18-1218/
http://repository.essex.ac.uk/23421/
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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
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