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    <titleInfo>
        <title>Identification of Alias Links among Participants in Narratives</title>
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    <name type="personal">
        <namePart type="given">Sangameshwar</namePart>
        <namePart type="family">Patil</namePart>
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    <name type="personal">
        <namePart type="given">Sachin</namePart>
        <namePart type="family">Pawar</namePart>
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    <name type="personal">
        <namePart type="given">Swapnil</namePart>
        <namePart type="family">Hingmire</namePart>
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    <name type="personal">
        <namePart type="given">Girish</namePart>
        <namePart type="family">Palshikar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    </name>
    <name type="personal">
        <namePart type="given">Vasudeva</namePart>
        <namePart type="family">Varma</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
        <namePart type="family">Bhattacharyya</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. In this paper, we propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. We use Markov Logic Network (MLN) to encode the linguistic knowledge for identification of aliases. We evaluate on four diverse history narratives of varying complexity. Our approach performs better than the state-of-the-art approach as well as a combination of standard named entity recognition and coreference resolution techniques.</abstract>
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%0 Conference Proceedings
%T Identification of Alias Links among Participants in Narratives
%A Patil, Sangameshwar
%A Pawar, Sachin
%A Hingmire, Swapnil
%A Palshikar, Girish
%A Varma, Vasudeva
%A Bhattacharyya, Pushpak
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F patil-etal-2018-identification
%X Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. In this paper, we propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. We use Markov Logic Network (MLN) to encode the linguistic knowledge for identification of aliases. We evaluate on four diverse history narratives of varying complexity. Our approach performs better than the state-of-the-art approach as well as a combination of standard named entity recognition and coreference resolution techniques.
%R 10.18653/v1/P18-2011
%U https://aclanthology.org/P18-2011
%U https://doi.org/10.18653/v1/P18-2011
%P 63-68

Markdown (Informal)

[Identification of Alias Links among Participants in Narratives](https://aclanthology.org/P18-2011) (Patil et al., ACL 2018)

• Identification of Alias Links among Participants in Narratives (Patil et al., ACL 2018)

ACL

• Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Girish Palshikar, Vasudeva Varma, and Pushpak Bhattacharyya. 2018. Identification of Alias Links among Participants in Narratives. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 63–68, Melbourne, Australia. Association for Computational Linguistics.