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    <titleInfo>
        <title>Derivational Morphological Relations in Word Embeddings</title>
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    <name type="personal">
        <namePart type="given">Tomáš</namePart>
        <namePart type="family">Musil</namePart>
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        <namePart type="given">Jonáš</namePart>
        <namePart type="family">Vidra</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <title>Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP</title>
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        <name type="personal">
            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
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        <name type="personal">
            <namePart type="given">Grzegorz</namePart>
            <namePart type="family">Chrupała</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yonatan</namePart>
            <namePart type="family">Belinkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Dieuwke</namePart>
            <namePart type="family">Hupkes</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes. In this paper, we explore the potential of word embeddings to identify properties of word derivations in the morphologically rich Czech language. We extract derivational relations between pairs of words from DeriNet, a Czech lexical network, which organizes almost one million Czech lemmas into derivational trees. For each such pair, we compute the difference of the embeddings of the two words, and perform unsupervised clustering of the resulting vectors. Our results show that these clusters largely match manually annotated semantic categories of the derivational relations (e.g. the relation ‘bake–baker’ belongs to category ‘actor’, and a correct clustering puts it into the same cluster as ‘govern–governor’).</abstract>
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    <identifier type="doi">10.18653/v1/W19-4818</identifier>
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        <url>https://aclanthology.org/W19-4818</url>
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    <part>
        <date>2019-08</date>
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            <start>173</start>
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%0 Conference Proceedings
%T Derivational Morphological Relations in Word Embeddings
%A Musil, Tomáš
%A Vidra, Jonáš
%A Mareček, David
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Belinkov, Yonatan
%Y Hupkes, Dieuwke
%S Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F musil-etal-2019-derivational
%X Derivation is a type of a word-formation process which creates new words from existing ones by adding, changing or deleting affixes. In this paper, we explore the potential of word embeddings to identify properties of word derivations in the morphologically rich Czech language. We extract derivational relations between pairs of words from DeriNet, a Czech lexical network, which organizes almost one million Czech lemmas into derivational trees. For each such pair, we compute the difference of the embeddings of the two words, and perform unsupervised clustering of the resulting vectors. Our results show that these clusters largely match manually annotated semantic categories of the derivational relations (e.g. the relation ‘bake–baker’ belongs to category ‘actor’, and a correct clustering puts it into the same cluster as ‘govern–governor’).
%R 10.18653/v1/W19-4818
%U https://aclanthology.org/W19-4818
%U https://doi.org/10.18653/v1/W19-4818
%P 173-180

Markdown (Informal)

[Derivational Morphological Relations in Word Embeddings](https://aclanthology.org/W19-4818) (Musil et al., BlackboxNLP 2019)

• Derivational Morphological Relations in Word Embeddings (Musil et al., BlackboxNLP 2019)

ACL

• Tomáš Musil, Jonáš Vidra, and David Mareček. 2019. Derivational Morphological Relations in Word Embeddings. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 173–180, Florence, Italy. Association for Computational Linguistics.