@inproceedings{aralikatte-sogaard-2020-model,
title = "Model-based Annotation of Coreference",
author = "Aralikatte, Rahul and
S{\o}gaard, Anders",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.9",
pages = "74--79",
abstract = "Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task {--} in our case limited to pronouns {--} into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task – in our case limited to pronouns – into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.</abstract>
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%0 Conference Proceedings
%T Model-based Annotation of Coreference
%A Aralikatte, Rahul
%A Søgaard, Anders
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F aralikatte-sogaard-2020-model
%X Humans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat unnatural task. This paper presents an alternative in which we preprocess documents, linking entities to a knowledge base, and turn the coreference annotation task – in our case limited to pronouns – into an annotation task where annotators are asked to assign pronouns to entities. Model-based annotation is shown to lead to faster annotation and higher inter-annotator agreement, and we argue that it also opens up an alternative approach to coreference resolution. We present two new coreference benchmark datasets, for English Wikipedia and English teacher-student dialogues, and evaluate state-of-the-art coreference resolvers on them.
%U https://aclanthology.org/2020.lrec-1.9
%P 74-79
Markdown (Informal)
[Model-based Annotation of Coreference](https://aclanthology.org/2020.lrec-1.9) (Aralikatte & Søgaard, LREC 2020)
ACL
- Rahul Aralikatte and Anders Søgaard. 2020. Model-based Annotation of Coreference. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 74–79, Marseille, France. European Language Resources Association.