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        <title>Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases</title>
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        <namePart type="given">Xing</namePart>
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            <title>Proceedings of the Workshop on Stylistic Variation</title>
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            <namePart type="given">Julian</namePart>
            <namePart type="family">Brooke</namePart>
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        <name type="personal">
            <namePart type="given">Moshe</namePart>
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    <abstract>Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications. In this work, we investigate whether salient dimensions of style variations are embedded in standard distributional vector spaces of word meaning. We hypothesizes that distances between embeddings of lexical paraphrases can help isolate style from meaning variations and help identify latent style dimensions. We conduct a qualitative analysis of latent style dimensions, and show the effectiveness of identified style subspaces on a lexical formality prediction task.</abstract>
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    <identifier type="doi">10.18653/v1/W17-4903</identifier>
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%0 Conference Proceedings
%T Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases
%A Niu, Xing
%A Carpuat, Marine
%Y Brooke, Julian
%Y Solorio, Thamar
%Y Koppel, Moshe
%S Proceedings of the Workshop on Stylistic Variation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F niu-carpuat-2017-discovering
%X Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications. In this work, we investigate whether salient dimensions of style variations are embedded in standard distributional vector spaces of word meaning. We hypothesizes that distances between embeddings of lexical paraphrases can help isolate style from meaning variations and help identify latent style dimensions. We conduct a qualitative analysis of latent style dimensions, and show the effectiveness of identified style subspaces on a lexical formality prediction task.
%R 10.18653/v1/W17-4903
%U https://aclanthology.org/W17-4903
%U https://doi.org/10.18653/v1/W17-4903
%P 20-27

Markdown (Informal)

[Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases](https://aclanthology.org/W17-4903) (Niu & Carpuat, Style-Var 2017)

• Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases (Niu & Carpuat, Style-Var 2017)

ACL

• Xing Niu and Marine Carpuat. 2017. Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases. In Proceedings of the Workshop on Stylistic Variation, pages 20–27, Copenhagen, Denmark. Association for Computational Linguistics.