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        <title>Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies</title>
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    <name type="personal">
        <namePart type="given">Andrey</namePart>
        <namePart type="family">Tagarev</namePart>
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    <name type="personal">
        <namePart type="given">Nikola</namePart>
        <namePart type="family">Tulechki</namePart>
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    <name type="personal">
        <namePart type="given">Svetla</namePart>
        <namePart type="family">Boytcheva</namePart>
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        <dateIssued>2019-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper addresses the task of categorizing companies within industry classification schemes. The datasets consists of encyclopedic articles about companies and their economic activities. The target classification schema is build by mapping linked open data in a semi-supervised manner. Target classes are build bottom-up from DBpedia. We apply several state of the art text classification techniques, based both on deep-learning and classical vector-space models.</abstract>
    <identifier type="citekey">tagarev-etal-2019-comparison</identifier>
    <identifier type="doi">10.26615/978-954-452-056-4_134</identifier>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies
%A Tagarev, Andrey
%A Tulechki, Nikola
%A Boytcheva, Svetla
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F tagarev-etal-2019-comparison
%X This paper addresses the task of categorizing companies within industry classification schemes. The datasets consists of encyclopedic articles about companies and their economic activities. The target classification schema is build by mapping linked open data in a semi-supervised manner. Target classes are build bottom-up from DBpedia. We apply several state of the art text classification techniques, based both on deep-learning and classical vector-space models.
%R 10.26615/978-954-452-056-4_134
%U https://aclanthology.org/R19-1134
%U https://doi.org/10.26615/978-954-452-056-4_134
%P 1169-1175

Markdown (Informal)

[Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies](https://aclanthology.org/R19-1134) (Tagarev et al., RANLP 2019)

• Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies (Tagarev et al., RANLP 2019)

ACL

• Andrey Tagarev, Nikola Tulechki, and Svetla Boytcheva. 2019. Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1169–1175, Varna, Bulgaria. INCOMA Ltd..