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Crowdsourcing and Aggregating Nested Markable Annotations ...
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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
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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.
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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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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
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Exploring Language Style in Chatbots to Increase Perceived Product Value and User Engagement
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A Probabilistic Annotation Model for Crowdsourcing Coreference
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Markup Infrastructure for the Anaphoric Bank: Supporting Web Collaboration
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