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    <titleInfo>
        <title>Applying Deep Neural Network to Retrieve Relevant Civil Law Articles</title>
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    <name type="personal">
        <namePart type="given">Anh</namePart>
        <namePart type="given">Hang</namePart>
        <namePart type="given">Nga</namePart>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2017</title>
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            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Pepa</namePart>
            <namePart type="family">Gencheva</namePart>
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        <name type="personal">
            <namePart type="given">Yasen</namePart>
            <namePart type="family">Kiprov</namePart>
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        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova</namePart>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>The paper aims to achieve the legal question answering information retrieval (IR) task at Competition on Legal Information Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the task is to utilize deep neural network, natural language processing and word2vec. The system was evaluated using training and testing data from the competition on legal information extraction/entailment (COLIEE). Our system mainly focuses on giving relevant civil law articles for given bar exams. The corpus of legal questions is drawn from Japanese Legal Bar exam queries. We implemented a combined deep neural network with additional features NLP and word2vec to gain the corresponding civil law articles based on a given bar exam ‘Yes/No’ questions. This paper focuses on clustering words-with-relation in order to acquire relevant civil law articles. All evaluation processes were done on the COLIEE 2017 training and test data set. The experimental result shows a very promising result.</abstract>
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    <identifier type="doi">10.26615/issn.1314-9156.2017_007</identifier>
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        <date>2017-09</date>
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            <start>46</start>
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%0 Conference Proceedings
%T Applying Deep Neural Network to Retrieve Relevant Civil Law Articles
%A Tran, Anh Hang Nga
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Gencheva, Pepa
%Y Kiprov, Yasen
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna
%F tran-2017-applying
%X The paper aims to achieve the legal question answering information retrieval (IR) task at Competition on Legal Information Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the task is to utilize deep neural network, natural language processing and word2vec. The system was evaluated using training and testing data from the competition on legal information extraction/entailment (COLIEE). Our system mainly focuses on giving relevant civil law articles for given bar exams. The corpus of legal questions is drawn from Japanese Legal Bar exam queries. We implemented a combined deep neural network with additional features NLP and word2vec to gain the corresponding civil law articles based on a given bar exam ‘Yes/No’ questions. This paper focuses on clustering words-with-relation in order to acquire relevant civil law articles. All evaluation processes were done on the COLIEE 2017 training and test data set. The experimental result shows a very promising result.
%R 10.26615/issn.1314-9156.2017_007
%U https://doi.org/10.26615/issn.1314-9156.2017_007
%P 46-48

Markdown (Informal)

[Applying Deep Neural Network to Retrieve Relevant Civil Law Articles](https://doi.org/10.26615/issn.1314-9156.2017_007) (Tran, RANLP 2017)

• Applying Deep Neural Network to Retrieve Relevant Civil Law Articles (Tran, RANLP 2017)

ACL

• Anh Hang Nga Tran. 2017. Applying Deep Neural Network to Retrieve Relevant Civil Law Articles. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 46–48, Varna. INCOMA Ltd..