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    <titleInfo>
        <title>A Sequence Learning Method for Domain-Specific Entity Linking</title>
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    <name type="personal">
        <namePart type="given">Emrah</namePart>
        <namePart type="family">Inan</namePart>
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    <name type="personal">
        <namePart type="given">Oguz</namePart>
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            <title>Proceedings of the Seventh Named Entities Workshop</title>
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        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Chen</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rafael</namePart>
            <namePart type="given">E</namePart>
            <namePart type="family">Banchs</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xiangyu</namePart>
            <namePart type="family">Duan</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Min</namePart>
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        <name type="personal">
            <namePart type="given">Haizhou</namePart>
            <namePart type="family">Li</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First, we match easy mention-entity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.</abstract>
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    <identifier type="doi">10.18653/v1/W18-2403</identifier>
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        <date>2018-07</date>
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            <start>14</start>
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%0 Conference Proceedings
%T A Sequence Learning Method for Domain-Specific Entity Linking
%A Inan, Emrah
%A Dikenelli, Oguz
%Y Chen, Nancy
%Y Banchs, Rafael E.
%Y Duan, Xiangyu
%Y Zhang, Min
%Y Li, Haizhou
%S Proceedings of the Seventh Named Entities Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F inan-dikenelli-2018-sequence
%X Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown to achieve remarkable results, they are computationally expensive for general datasets. Also, semantic embeddings only indicate relatedness between entity pairs without considering sequences. In this paper, we address these problems by introducing a two-fold neural model. First, we match easy mention-entity pairs and using the domain information of this pair to filter candidate entities of closer mentions. Second, we resolve more ambiguous pairs using bidirectional Long Short-Term Memory and CRF models for the entity disambiguation. Our proposed system outperforms state-of-the-art systems on the generated domain-specific evaluation dataset.
%R 10.18653/v1/W18-2403
%U https://aclanthology.org/W18-2403
%U https://doi.org/10.18653/v1/W18-2403
%P 14-21

Markdown (Informal)

[A Sequence Learning Method for Domain-Specific Entity Linking](https://aclanthology.org/W18-2403) (Inan & Dikenelli, NEWS 2018)

• A Sequence Learning Method for Domain-Specific Entity Linking (Inan & Dikenelli, NEWS 2018)

ACL

• Emrah Inan and Oguz Dikenelli. 2018. A Sequence Learning Method for Domain-Specific Entity Linking. In Proceedings of the Seventh Named Entities Workshop, pages 14–21, Melbourne, Australia. Association for Computational Linguistics.