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    <titleInfo>
        <title>Recognizing Complex Entity Mentions: A Review and Future Directions</title>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
        <namePart type="family">Dai</namePart>
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            <title>Proceedings of ACL 2018, Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Vered</namePart>
            <namePart type="family">Shwartz</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jeniya</namePart>
            <namePart type="family">Tabassum</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rob</namePart>
            <namePart type="family">Voigt</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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        <name type="personal">
            <namePart type="given">Marie-Catherine</namePart>
            <namePart type="family">de Marneffe</namePart>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.</abstract>
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    <identifier type="doi">10.18653/v1/P18-3006</identifier>
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%0 Conference Proceedings
%T Recognizing Complex Entity Mentions: A Review and Future Directions
%A Dai, Xiang
%Y Shwartz, Vered
%Y Tabassum, Jeniya
%Y Voigt, Rob
%Y Che, Wanxiang
%Y de Marneffe, Marie-Catherine
%Y Nissim, Malvina
%S Proceedings of ACL 2018, Student Research Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F dai-2018-recognizing
%X Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.
%R 10.18653/v1/P18-3006
%U https://aclanthology.org/P18-3006
%U https://doi.org/10.18653/v1/P18-3006
%P 37-44

Markdown (Informal)

[Recognizing Complex Entity Mentions: A Review and Future Directions](https://aclanthology.org/P18-3006) (Dai, ACL 2018)

• Recognizing Complex Entity Mentions: A Review and Future Directions (Dai, ACL 2018)

ACL

• Xiang Dai. 2018. Recognizing Complex Entity Mentions: A Review and Future Directions. In Proceedings of ACL 2018, Student Research Workshop, pages 37–44, Melbourne, Australia. Association for Computational Linguistics.