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        <title>An annotated dataset of literary entities</title>
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        <namePart type="given">David</namePart>
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    <abstract>We present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). These categories include non-named entities (such as “the boy”, “the kitchen”) and nested structure (such as [[the cook]’s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain literary data yields an improvement of over 20 absolute points in F-score (from 45.7 to 68.3), and mitigates a disparate impact in performance for male and female entities present in models trained on news data.</abstract>
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%0 Conference Proceedings
%T An annotated dataset of literary entities
%A Bamman, David
%A Popat, Sejal
%A Shen, Sheng
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F bamman-etal-2019-annotated
%X We present a new dataset comprised of 210,532 tokens evenly drawn from 100 different English-language literary texts annotated for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). These categories include non-named entities (such as “the boy”, “the kitchen”) and nested structure (such as [[the cook]’s sister]). In contrast to existing datasets built primarily on news (focused on geo-political entities and organizations), literary texts offer strikingly different distributions of entity categories, with much stronger emphasis on people and description of settings. We present empirical results demonstrating the performance of nested entity recognition models in this domain; training natively on in-domain literary data yields an improvement of over 20 absolute points in F-score (from 45.7 to 68.3), and mitigates a disparate impact in performance for male and female entities present in models trained on news data.
%R 10.18653/v1/N19-1220
%U https://aclanthology.org/N19-1220
%U https://doi.org/10.18653/v1/N19-1220
%P 2138-2144

Markdown (Informal)

[An annotated dataset of literary entities](https://aclanthology.org/N19-1220) (Bamman et al., NAACL 2019)

• An annotated dataset of literary entities (Bamman et al., NAACL 2019)

ACL

• David Bamman, Sejal Popat, and Sheng Shen. 2019. An annotated dataset of literary entities. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2138–2144, Minneapolis, Minnesota. Association for Computational Linguistics.