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        <title>A Document Descriptor using Covariance of Word Vectors</title>
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        <namePart type="given">Marwan</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>In this paper, we address the problem of finding a novel document descriptor based on the covariance matrix of the word vectors of a document. Our descriptor has a fixed length, which makes it easy to use in many supervised and unsupervised applications. We tested our novel descriptor in different tasks including supervised and unsupervised settings. Our evaluation shows that our document covariance descriptor fits different tasks with competitive performance against state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2084</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T A Document Descriptor using Covariance of Word Vectors
%A Torki, Marwan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F torki-2018-document
%X In this paper, we address the problem of finding a novel document descriptor based on the covariance matrix of the word vectors of a document. Our descriptor has a fixed length, which makes it easy to use in many supervised and unsupervised applications. We tested our novel descriptor in different tasks including supervised and unsupervised settings. Our evaluation shows that our document covariance descriptor fits different tasks with competitive performance against state-of-the-art methods.
%R 10.18653/v1/P18-2084
%U https://aclanthology.org/P18-2084
%U https://doi.org/10.18653/v1/P18-2084
%P 527-532

Markdown (Informal)

[A Document Descriptor using Covariance of Word Vectors](https://aclanthology.org/P18-2084) (Torki, ACL 2018)

• A Document Descriptor using Covariance of Word Vectors (Torki, ACL 2018)

ACL

• Marwan Torki. 2018. A Document Descriptor using Covariance of Word Vectors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 527–532, Melbourne, Australia. Association for Computational Linguistics.