<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="otto-2018-team">
    <titleInfo>
        <title>Team GESIS Cologne: An all in all sentence-based approach for FEVER</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Wolfgang</namePart>
        <namePart type="family">Otto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach. Throughout all steps of our pipeline, we regarded single sentences as our processing unit. In our IR-Component, we searched in the set of all possible Wikipedia introduction sentences without limiting sentences to a fixed number of relevant documents. In the entailment module, we judged every sentence separately and combined the result of the classifier for the top 5 sentences with the help of an ensemble classifier to make a judgment whether the truth of a statement can be derived from the given claim.</abstract>
    <identifier type="citekey">otto-2018-team</identifier>
    <identifier type="doi">10.18653/v1/W18-5524</identifier>
    <location>
        <url>https://aclanthology.org/W18-5524</url>
    </location>
    <part>
        <date>2018-nov</date>
        <extent unit="page">
            <start>145</start>
            <end>149</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Team GESIS Cologne: An all in all sentence-based approach for FEVER
%A Otto, Wolfgang
%S Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
%D 2018
%8 nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F otto-2018-team
%X In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach. Throughout all steps of our pipeline, we regarded single sentences as our processing unit. In our IR-Component, we searched in the set of all possible Wikipedia introduction sentences without limiting sentences to a fixed number of relevant documents. In the entailment module, we judged every sentence separately and combined the result of the classifier for the top 5 sentences with the help of an ensemble classifier to make a judgment whether the truth of a statement can be derived from the given claim.
%R 10.18653/v1/W18-5524
%U https://aclanthology.org/W18-5524
%U https://doi.org/10.18653/v1/W18-5524
%P 145-149

Markdown (Informal)

[Team GESIS Cologne: An all in all sentence-based approach for FEVER](https://aclanthology.org/W18-5524) (Otto, 2018)

• Team GESIS Cologne: An all in all sentence-based approach for FEVER (Otto, 2018)

ACL

• Wolfgang Otto. 2018. Team GESIS Cologne: An all in all sentence-based approach for FEVER. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 145–149, Brussels, Belgium. Association for Computational Linguistics.