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    <titleInfo>
        <title>WordNet-based similarity metrics for adjectives</title>
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    <name type="personal">
        <namePart type="given">Emiel</namePart>
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        <dateIssued>2016-27–30 jan</dateIssued>
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            <title>Proceedings of the 8th Global WordNet Conference (GWC)</title>
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        <name type="personal">
            <namePart type="given">Christiane</namePart>
            <namePart type="family">Fellbaum</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Piek</namePart>
            <namePart type="family">Vossen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Verginica</namePart>
            <namePart type="given">Barbu</namePart>
            <namePart type="family">Mititelu</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Corina</namePart>
            <namePart type="family">Forascu</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Global Wordnet Association</publisher>
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    <abstract>Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.</abstract>
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        <url>https://aclanthology.org/2016.gwc-1.58</url>
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    <part>
        <date>2016-27–30 jan</date>
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            <start>419</start>
            <end>423</end>
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%0 Conference Proceedings
%T WordNet-based similarity metrics for adjectives
%A van Miltenburg, Emiel
%Y Fellbaum, Christiane
%Y Vossen, Piek
%Y Mititelu, Verginica Barbu
%Y Forascu, Corina
%S Proceedings of the 8th Global WordNet Conference (GWC)
%D 2016
%8 27–30 jan
%I Global Wordnet Association
%C Bucharest, Romania
%F van-miltenburg-2016-wordnet
%X Le and Fokkens (2015) recently showed that taxonomy-based approaches are more reliable than corpus-based approaches in estimating human similarity ratings. On the other hand, distributional models provide much better coverage. The lack of an established similarity metric for adjectives in WordNet is a case in point. I present initial work to establish such a metric, and propose ways to move forward by looking at extensions to WordNet. I show that the shortest path distance between derivationally related forms provides a reliable estimate of adjective similarity. Furthermore, I find that a hybrid method combining this measure with vector-based similarity estimations gives us the best of both worlds: more reliable similarity estimations than vectors alone, but with the same coverage as corpus-based methods.
%U https://aclanthology.org/2016.gwc-1.58
%P 419-423

Markdown (Informal)

[WordNet-based similarity metrics for adjectives](https://aclanthology.org/2016.gwc-1.58) (van Miltenburg, GWC 2016)

• WordNet-based similarity metrics for adjectives (van Miltenburg, GWC 2016)

ACL

• Emiel van Miltenburg. 2016. WordNet-based similarity metrics for adjectives. In Proceedings of the 8th Global WordNet Conference (GWC), pages 419–423, Bucharest, Romania. Global Wordnet Association.