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Simple Search
Hits 1 – 12 of 12
1
Neural Coreference Resolution for Arabic ...
The 28th International Conference on Computational Linguistics 2020
;
Aloraini, Abdulrahman
;
Poesio, Massimo
. - : Underline Science Inc., 2020
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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
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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
BASE
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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
;
Chamberlain, Jon
;
Kruschwitz, Udo
;
Yu, Juntao
. - : Association for Computational Linguistics, 2019
Abstract:
We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.
URL:
http://repository.essex.ac.uk/25795/
https://doi.org/10.18653/v1/N19-1176
http://repository.essex.ac.uk/25795/7/N19-1176.pdf
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8
Crowdsourcing and Aggregating Nested Markable Annotations
Poesio, Massimo
;
Yu, Juntao
;
Chamberlain, Jon
. - : Association for Computational Linguistics, 2019
BASE
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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
BASE
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10
Semi-Supervised Methods for Out-of-Domain Dependency Parsing ...
Yu, Juntao
. - : arXiv, 2018
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11
Semi-supervised methods for out-of-domain dependency parsing
Yu, Juntao
. - 2018
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12
A Probabilistic Annotation Model for Crowdsourcing Coreference
Kruschwitz, Udo
;
Chamberlain, Jon
;
Yu, Juntao
. - : Association for Computational Linguistics, 2018
BASE
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