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    <titleInfo>
        <title>Clustering Issues in Civil Judgments for Recommending Similar Cases</title>
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    <name type="personal">
        <namePart type="given">Yi-Fan</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Chao-Lin</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Chieh</namePart>
        <namePart type="family">Yang</namePart>
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        <dateIssued>2022-11</dateIssued>
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            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
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            <publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
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    <abstract>Similar judgments search is an important task in legal practice, from which valuable legal insights can be obtained. Issues are disputes between both parties in civil litigation, which represents the core topics to be considered in the trials. Many studies calculate the similarity between judgments from different perspectives and methods. We first cluster the issues in the judgments, and then encode the judgments with vectors for whether or not the judgments contain issues in the corresponding clusters. The similarity between the judgments are evaluated based on the encoded messages. We verify the effectiveness of the system with a human scoring process by a legal background assistant, while comparing the effects of several combinations of preprocessing steps and selections of clustering strategies.</abstract>
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        <url>https://aclanthology.org/2022.rocling-1.23</url>
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    <part>
        <date>2022-11</date>
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            <start>184</start>
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%0 Conference Proceedings
%T Clustering Issues in Civil Judgments for Recommending Similar Cases
%A Liu, Yi-Fan
%A Liu, Chao-Lin
%A Yang, Chieh
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F liu-etal-2022-clustering
%X Similar judgments search is an important task in legal practice, from which valuable legal insights can be obtained. Issues are disputes between both parties in civil litigation, which represents the core topics to be considered in the trials. Many studies calculate the similarity between judgments from different perspectives and methods. We first cluster the issues in the judgments, and then encode the judgments with vectors for whether or not the judgments contain issues in the corresponding clusters. The similarity between the judgments are evaluated based on the encoded messages. We verify the effectiveness of the system with a human scoring process by a legal background assistant, while comparing the effects of several combinations of preprocessing steps and selections of clustering strategies.
%U https://aclanthology.org/2022.rocling-1.23
%P 184-192

Markdown (Informal)

[Clustering Issues in Civil Judgments for Recommending Similar Cases](https://aclanthology.org/2022.rocling-1.23) (Liu et al., ROCLING 2022)

• Clustering Issues in Civil Judgments for Recommending Similar Cases (Liu et al., ROCLING 2022)

ACL

• Yi-Fan Liu, Chao-Lin Liu, and Chieh Yang. 2022. Clustering Issues in Civil Judgments for Recommending Similar Cases. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 184–192, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).