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        <title>An Effective Transition-based Model for Discontinuous NER</title>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
        <namePart type="family">Dai</namePart>
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        <namePart type="given">Sarvnaz</namePart>
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    <name type="personal">
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            <namePart type="given">Dan</namePart>
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    <abstract>Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.</abstract>
    <identifier type="citekey">dai-etal-2020-effective</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.520</identifier>
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%0 Conference Proceedings
%T An Effective Transition-based Model for Discontinuous NER
%A Dai, Xiang
%A Karimi, Sarvnaz
%A Hachey, Ben
%A Paris, Cecile
%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 dai-etal-2020-effective
%X Unlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions that are efficient but preclude recovery of these mentions. We propose a simple, effective transition-based model with generic neural encoding for discontinuous NER. Through extensive experiments on three biomedical data sets, we show that our model can effectively recognize discontinuous mentions without sacrificing the accuracy on continuous mentions.
%R 10.18653/v1/2020.acl-main.520
%U https://aclanthology.org/2020.acl-main.520
%U https://doi.org/10.18653/v1/2020.acl-main.520
%P 5860-5870

Markdown (Informal)

[An Effective Transition-based Model for Discontinuous NER](https://aclanthology.org/2020.acl-main.520) (Dai et al., ACL 2020)

• An Effective Transition-based Model for Discontinuous NER (Dai et al., ACL 2020)

ACL

• Xiang Dai, Sarvnaz Karimi, Ben Hachey, and Cecile Paris. 2020. An Effective Transition-based Model for Discontinuous NER. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5860–5870, Online. Association for Computational Linguistics.