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        <title>Named Entity Recognition on Code-Switched Data Using Conditional Random Fields</title>
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        <namePart type="given">Utpal</namePart>
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        <namePart type="given">Biswanath</namePart>
        <namePart type="family">Barik</namePart>
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            <title>Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching</title>
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            <namePart type="given">Gustavo</namePart>
            <namePart type="family">Aguilar</namePart>
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        <name type="personal">
            <namePart type="given">Fahad</namePart>
            <namePart type="family">AlGhamdi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Victor</namePart>
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            <namePart type="given">Thamar</namePart>
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        <name type="personal">
            <namePart type="given">Mona</namePart>
            <namePart type="family">Diab</namePart>
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            <namePart type="given">Julia</namePart>
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    <abstract>Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25% F1-score.</abstract>
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    <identifier type="doi">10.18653/v1/W18-3215</identifier>
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%0 Conference Proceedings
%T Named Entity Recognition on Code-Switched Data Using Conditional Random Fields
%A Sikdar, Utpal Kumar
%A Barik, Biswanath
%A Gambäck, Björn
%Y Aguilar, Gustavo
%Y AlGhamdi, Fahad
%Y Soto, Victor
%Y Solorio, Thamar
%Y Diab, Mona
%Y Hirschberg, Julia
%S Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sikdar-etal-2018-named
%X Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25% F1-score.
%R 10.18653/v1/W18-3215
%U https://aclanthology.org/W18-3215
%U https://doi.org/10.18653/v1/W18-3215
%P 115-119

Markdown (Informal)

[Named Entity Recognition on Code-Switched Data Using Conditional Random Fields](https://aclanthology.org/W18-3215) (Sikdar et al., ACL 2018)

• Named Entity Recognition on Code-Switched Data Using Conditional Random Fields (Sikdar et al., ACL 2018)

ACL

• Utpal Kumar Sikdar, Biswanath Barik, and Björn Gambäck. 2018. Named Entity Recognition on Code-Switched Data Using Conditional Random Fields. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 115–119, Melbourne, Australia. Association for Computational Linguistics.