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        <title>Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER</title>
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    <name type="personal">
        <namePart type="given">An</namePart>
        <namePart type="family">Nguyen Le</namePart>
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    <name type="personal">
        <namePart type="given">Hajime</namePart>
        <namePart type="family">Morita</namePart>
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    <name type="personal">
        <namePart type="given">Tomoya</namePart>
        <namePart type="family">Iwakura</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage. However, the usual approximate matching approach fetches only one matching result, which is often noisy. In this work, we propose a method for biomedical NER that fetches multiple approximate matches for a given phrase to leverage their variations to estimate entity-likeness. The model uses pooling to discard the unnecessary information from the noisy matching results, and learn the entity-likeness of the phrase with multiple approximate matches. Experimental results on three benchmark datasets from the biomedical domain, BC2GM, NCBI-disease, and BC4CHEMD, demonstrate the effectiveness. Our model improves the average by up to +0.21 points compared to a BioBERT-based NER.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.117</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER
%A Nguyen Le, An
%A Morita, Hajime
%A Iwakura, Tomoya
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F nguyen-le-etal-2021-learning
%X Biomedical Named Entities are complex, so approximate matching has been used to improve entity coverage. However, the usual approximate matching approach fetches only one matching result, which is often noisy. In this work, we propose a method for biomedical NER that fetches multiple approximate matches for a given phrase to leverage their variations to estimate entity-likeness. The model uses pooling to discard the unnecessary information from the noisy matching results, and learn the entity-likeness of the phrase with multiple approximate matches. Experimental results on three benchmark datasets from the biomedical domain, BC2GM, NCBI-disease, and BC4CHEMD, demonstrate the effectiveness. Our model improves the average by up to +0.21 points compared to a BioBERT-based NER.
%U https://aclanthology.org/2021.ranlp-1.117
%P 1040-1049

Markdown (Informal)

[Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER](https://aclanthology.org/2021.ranlp-1.117) (Nguyen Le et al., RANLP 2021)

• Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER (Nguyen Le et al., RANLP 2021)

ACL

• An Nguyen Le, Hajime Morita, and Tomoya Iwakura. 2021. Learning Entity-Likeness with Multiple Approximate Matches for Biomedical NER. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1040–1049, Held Online. INCOMA Ltd..