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    <titleInfo>
        <title>Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</title>
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    <name type="personal">
        <namePart type="given">Dmitry</namePart>
        <namePart type="family">Ustalov</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Panchenko</namePart>
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    <name type="personal">
        <namePart type="given">Chris</namePart>
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        <namePart type="given">Simone</namePart>
        <namePart type="given">Paolo</namePart>
        <namePart type="family">Ponzetto</namePart>
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    <abstract>We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.</abstract>
    <identifier type="citekey">ustalov-etal-2019-watset</identifier>
    <identifier type="doi">10.1162/coli_a_00354</identifier>
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        <url>https://aclanthology.org/J19-3002</url>
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        <date>2019-09</date>
        <detail type="volume"><number>45</number></detail>
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            <start>423</start>
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%0 Journal Article
%T Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
%A Ustalov, Dmitry
%A Panchenko, Alexander
%A Biemann, Chris
%A Ponzetto, Simone Paolo
%J Computational Linguistics
%D 2019
%8 September
%V 45
%N 3
%I MIT Press
%C Cambridge, MA
%F ustalov-etal-2019-watset
%X We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph, which reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can also be applied to other networks of linguistic data.
%R 10.1162/coli_a_00354
%U https://aclanthology.org/J19-3002
%U https://doi.org/10.1162/coli_a_00354
%P 423-479

Markdown (Informal)

[Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction](https://aclanthology.org/J19-3002) (Ustalov et al., CL 2019)

• Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction (Ustalov et al., CL 2019)

ACL

• Dmitry Ustalov, Alexander Panchenko, Chris Biemann, and Simone Paolo Ponzetto. 2019. Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction. Computational Linguistics, 45(3):423–479.