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    <titleInfo>
        <title>Named Entity Recognition in Information Security Domain for Russian</title>
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    <name type="personal">
        <namePart type="given">Anastasiia</namePart>
        <namePart type="family">Sirotina</namePart>
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    <name type="personal">
        <namePart type="given">Natalia</namePart>
        <namePart type="family">Loukachevitch</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>INCOMA Ltd.</publisher>
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    <abstract>In this paper we discuss the named entity recognition task for Russian texts related to cybersecurity. First of all, we describe the problems that arise in course of labeling unstructured texts from information security domain. We introduce guidelines for human annotators, according to which a corpus has been marked up. Then, a CRF-based system and different neural architectures have been implemented and applied to the corpus. The named entity recognition systems have been evaluated and compared to determine the most efficient one.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_128</identifier>
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        <url>https://aclanthology.org/R19-1128</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Named Entity Recognition in Information Security Domain for Russian
%A Sirotina, Anastasiia
%A Loukachevitch, Natalia
%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 sirotina-loukachevitch-2019-named
%X In this paper we discuss the named entity recognition task for Russian texts related to cybersecurity. First of all, we describe the problems that arise in course of labeling unstructured texts from information security domain. We introduce guidelines for human annotators, according to which a corpus has been marked up. Then, a CRF-based system and different neural architectures have been implemented and applied to the corpus. The named entity recognition systems have been evaluated and compared to determine the most efficient one.
%R 10.26615/978-954-452-056-4_128
%U https://aclanthology.org/R19-1128
%U https://doi.org/10.26615/978-954-452-056-4_128
%P 1114-1120

Markdown (Informal)

[Named Entity Recognition in Information Security Domain for Russian](https://aclanthology.org/R19-1128) (Sirotina & Loukachevitch, RANLP 2019)

• Named Entity Recognition in Information Security Domain for Russian (Sirotina & Loukachevitch, RANLP 2019)

ACL

• Anastasiia Sirotina and Natalia Loukachevitch. 2019. Named Entity Recognition in Information Security Domain for Russian. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1114–1120, Varna, Bulgaria. INCOMA Ltd..