<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="becker-etal-2021-coco">
    <titleInfo>
        <title>COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Becker</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Katharina</namePart>
        <namePart type="family">Korfhage</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anette</namePart>
        <namePart type="family">Frank</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-apr</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that – unlike conventional knowledge graphs – nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.</abstract>
    <identifier type="citekey">becker-etal-2021-coco</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-demos.15</identifier>
    <location>
        <url>https://aclanthology.org/2021.eacl-demos.15</url>
    </location>
    <part>
        <date>2021-apr</date>
        <extent unit="page">
            <start>119</start>
            <end>126</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet
%A Becker, Maria
%A Korfhage, Katharina
%A Frank, Anette
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F becker-etal-2021-coco
%X In this paper we present COCO-EX, a tool for Extracting Concepts from texts and linking them to the ConceptNet knowledge graph. COCO-EX extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. COCOEX takes into account the challenging characteristics of ConceptNet, namely that – unlike conventional knowledge graphs – nodes are represented as non-canonicalized, free-form text. This means that i) concepts are not normalized; ii) they often consist of several different, nested phrase types; and iii) many of them are uninformative, over-specific, or misspelled. A commonly used shortcut to circumvent these problems is to apply string matching. We compare COCO-EX to this method and show that COCO-EX enables the extraction of meaningful, important rather than overspecific or uninformative concepts, and allows to assess more relational information stored in the knowledge graph.
%R 10.18653/v1/2021.eacl-demos.15
%U https://aclanthology.org/2021.eacl-demos.15
%U https://doi.org/10.18653/v1/2021.eacl-demos.15
%P 119-126

Markdown (Informal)

[COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet](https://aclanthology.org/2021.eacl-demos.15) (Becker et al., EACL 2021)

• COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet (Becker et al., EACL 2021)

ACL

• Maria Becker, Katharina Korfhage, and Anette Frank. 2021. COCO-EX: A Tool for Linking Concepts from Texts to ConceptNet. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 119–126, Online. Association for Computational Linguistics.