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    <titleInfo>
        <title>Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms</title>
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    <name type="personal">
        <namePart type="given">Senyu</namePart>
        <namePart type="family">Li</namePart>
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    <name type="personal">
        <namePart type="given">Bradley</namePart>
        <namePart type="family">Hauer</namePart>
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    <name type="personal">
        <namePart type="given">Ning</namePart>
        <namePart type="family">Shi</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Grzegorz</namePart>
        <namePart type="family">Kondrak</namePart>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Constructing lexicons with explicitly identified lexical gaps is a vital part of building multilingual lexical resources. Prior work has leveraged bilingual dictionaries and linguistic typologies for semi-automatic identification of lexical gaps. Instead, we propose a generally-applicable algorithmic method to automatically generate concept lexicalizations, which is based on machine translation and hypernymy relations between concepts. The absence of a lexicalization implies a lexical gap. We apply our method to kinship terms, which make a suitable case study because of their explicit definitions and regular structure. Empirical evaluations demonstrate that our approach yields higher accuracy than BabelNet and ChatGPT. Our error analysis indicates that enhancing the quality of translations can further improve the accuracy of our method.</abstract>
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        <url>https://aclanthology.org/2024.acl-long.372</url>
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        <date>2024-08</date>
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            <start>6891</start>
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%0 Conference Proceedings
%T Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms
%A Li, Senyu
%A Hauer, Bradley
%A Shi, Ning
%A Kondrak, Grzegorz
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F li-etal-2024-translation
%X Constructing lexicons with explicitly identified lexical gaps is a vital part of building multilingual lexical resources. Prior work has leveraged bilingual dictionaries and linguistic typologies for semi-automatic identification of lexical gaps. Instead, we propose a generally-applicable algorithmic method to automatically generate concept lexicalizations, which is based on machine translation and hypernymy relations between concepts. The absence of a lexicalization implies a lexical gap. We apply our method to kinship terms, which make a suitable case study because of their explicit definitions and regular structure. Empirical evaluations demonstrate that our approach yields higher accuracy than BabelNet and ChatGPT. Our error analysis indicates that enhancing the quality of translations can further improve the accuracy of our method.
%U https://aclanthology.org/2024.acl-long.372
%P 6891-6900

Markdown (Informal)

[Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms](https://aclanthology.org/2024.acl-long.372) (Li et al., ACL 2024)

• Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms (Li et al., ACL 2024)

ACL

• Senyu Li, Bradley Hauer, Ning Shi, and Grzegorz Kondrak. 2024. Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6891–6900, Bangkok, Thailand. Association for Computational Linguistics.