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            <title xml:lang="en">Predicting the Outcome of Appeal Decisions in Germany’s Tax Law</title>
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                <forename type="first">Bernhard</forename>
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                <forename type="first">Elena</forename>
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                <title xml:lang="en">Predicting the Outcome of Appeal Decisions in Germany’s Tax Law</title>
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                  <persName>
                    <forename type="first">Bernhard</forename>
                    <surname>Waltl</surname>
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                    <forename type="first">Georg</forename>
                    <surname>Bonczek</surname>
                  </persName>
                  <idno type="halauthorid">1309716-0</idno>
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                <author role="aut">
                  <persName>
                    <forename type="first">Elena</forename>
                    <surname>Scepankova</surname>
                  </persName>
                  <idno type="halauthorid">1309717-0</idno>
                  <affiliation ref="#struct-132871"/>
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                <author role="aut">
                  <persName>
                    <forename type="first">Jörg</forename>
                    <surname>Landthaler</surname>
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                  <idno type="halauthorid">1309718-0</idno>
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                <author role="aut">
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                    <forename type="first">Florian</forename>
                    <surname>Matthes</surname>
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                  <idno type="halauthorid">1051971-0</idno>
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                  <title>9th International Conference on Electronic Participation (ePart)</title>
                  <date type="start">2017-09-04</date>
                  <date type="end">2017-09-07</date>
                  <settlement>St. Petersburg</settlement>
                  <country key="RU">Russia</country>
                </meeting>
                <editor>Peter Parycek</editor>
                <editor>Yannis Charalabidis</editor>
                <editor>Andrei V. Chugunov</editor>
                <editor>Panos Panagiotopoulos</editor>
                <editor>Theresa A. Pardo</editor>
                <editor>Øystein Sæbø</editor>
                <editor>Efthimios Tambouris</editor>
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                  <publisher>Springer International Publishing</publisher>
                  <biblScope unit="serie">Electronic Participation</biblScope>
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                  <biblScope unit="pp">89-99</biblScope>
                  <date type="datePub">2017</date>
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              <p>Predicting the outcome or the probability of winning a legal case has always been highly attractive in legal sciences and practice. Hardly any attempt has been made to predict the outcome of German cases, although prior court decisions become more and more important in various legal domains of Germany’s jurisdiction, e.g., tax law.This paper summarizes our research on training a machine learning classifier to determine likelihood ratios and thus predict the outcome of a restricted set of cases from Germany’s jurisdiction. Based on a data set of German tax law cases (44 285 documents from 1945 to 2016) we selected those cases which belong to an appeal decision (5 990 documents). We used the provided meta-data and natural language processing to extract 11 relevant features and trained a Naive Bayes classifier to predict whether an appeal is going to be successful or not.The evaluation (10-fold cross validation) on the data set has shown a performance regarding $$\text {F}_1$$-score between 0.53 and 0.58. This score indicates that there is room for improvement. We expect that the high relevancy for legal practice, the availability of data, and advance machine learning techniques will foster more research in this area.</p>
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              <org type="consortium">TC 8</org>
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          <orgName>Technische Universität Munchen = Technical University Munich = Université Technique de Munich</orgName>
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          <date type="start">1868-01-01</date>
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              <addrLine>Arcisstrasse 21, D- 80333 München</addrLine>
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