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        <title>Soft Gazetteers for Low-Resource Named Entity Recognition</title>
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        <namePart type="given">Shruti</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.</abstract>
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%0 Conference Proceedings
%T Soft Gazetteers for Low-Resource Named Entity Recognition
%A Rijhwani, Shruti
%A Zhou, Shuyan
%A Neubig, Graham
%A Carbonell, Jaime
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F rijhwani-etal-2020-soft
%X Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of “soft gazetteers” that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.
%R 10.18653/v1/2020.acl-main.722
%U https://aclanthology.org/2020.acl-main.722/
%U https://doi.org/10.18653/v1/2020.acl-main.722
%P 8118-8123

Markdown (Informal)

[Soft Gazetteers for Low-Resource Named Entity Recognition](https://aclanthology.org/2020.acl-main.722/) (Rijhwani et al., ACL 2020)

• Soft Gazetteers for Low-Resource Named Entity Recognition (Rijhwani et al., ACL 2020)

ACL

• Shruti Rijhwani, Shuyan Zhou, Graham Neubig, and Jaime Carbonell. 2020. Soft Gazetteers for Low-Resource Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8118–8123, Online. Association for Computational Linguistics.