<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sulea-etal-2017-predicting">
    <titleInfo>
        <title>Predicting the Law Area and Decisions of French Supreme Court Cases</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Octavia-Maria</namePart>
        <namePart type="family">Şulea</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marcos</namePart>
        <namePart type="family">Zampieri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mihaela</namePart>
        <namePart type="family">Vela</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Josef</namePart>
        <namePart type="family">van Genabith</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge’s motivation for a ruling to emulate a real-world test scenario. We report results of 96% f1 score in predicting a case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features.</abstract>
    <identifier type="citekey">sulea-etal-2017-predicting</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_092</identifier>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>716</start>
            <end>722</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Predicting the Law Area and Decisions of French Supreme Court Cases
%A Şulea, Octavia-Maria
%A Zampieri, Marcos
%A Vela, Mihaela
%A van Genabith, Josef
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F sulea-etal-2017-predicting
%X In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge’s motivation for a ruling to emulate a real-world test scenario. We report results of 96% f1 score in predicting a case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features.
%R 10.26615/978-954-452-049-6_092
%U https://doi.org/10.26615/978-954-452-049-6_092
%P 716-722

Markdown (Informal)

[Predicting the Law Area and Decisions of French Supreme Court Cases](https://doi.org/10.26615/978-954-452-049-6_092) (Şulea et al., RANLP 2017)

• Predicting the Law Area and Decisions of French Supreme Court Cases (Şulea et al., RANLP 2017)

ACL

• Octavia-Maria Şulea, Marcos Zampieri, Mihaela Vela, and Josef van Genabith. 2017. Predicting the Law Area and Decisions of French Supreme Court Cases. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 716–722, Varna, Bulgaria. INCOMA Ltd..