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        <title>An Analysis of Simple Data Augmentation for Named Entity Recognition</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">Heike</namePart>
        <namePart type="family">Adel</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
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    <abstract>Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.</abstract>
    <identifier type="citekey">dai-adel-2020-analysis</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.343</identifier>
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%0 Conference Proceedings
%T An Analysis of Simple Data Augmentation for Named Entity Recognition
%A Dai, Xiang
%A Adel, Heike
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F dai-adel-2020-analysis
%X Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.
%R 10.18653/v1/2020.coling-main.343
%U https://aclanthology.org/2020.coling-main.343
%U https://doi.org/10.18653/v1/2020.coling-main.343
%P 3861-3867

Markdown (Informal)

[An Analysis of Simple Data Augmentation for Named Entity Recognition](https://aclanthology.org/2020.coling-main.343) (Dai & Adel, COLING 2020)

• An Analysis of Simple Data Augmentation for Named Entity Recognition (Dai & Adel, COLING 2020)

ACL

• Xiang Dai and Heike Adel. 2020. An Analysis of Simple Data Augmentation for Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3861–3867, Barcelona, Spain (Online). International Committee on Computational Linguistics.