DCU-UVT.

Word-Level Language Classification with Code-Mixed Data

Utsab Barman, Joachim Wagner, Grzegorz Chrupala, Jennifer Foster

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientific

    Abstract

    This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments,
    we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.
    Original languageEnglish
    Title of host publicationFirst Workshop on Computational Approaches to Code Switching
    PublisherAssociation for Computational Linguistics (ACL)
    Pages127-132
    Number of pages6
    ISBN (Electronic)9781937284961
    Publication statusPublished - 2014
    EventConference on Empirical Methods in Natural Language Processing - Doha, Qatar
    Duration: 25 Oct 201429 Oct 2014

    Conference

    ConferenceConference on Empirical Methods in Natural Language Processing
    CountryQatar
    CityDoha
    Period25/10/1429/10/14

    Fingerprint

    Support vector machines
    Glossaries
    Experiments

    Cite this

    Barman, U., Wagner, J., Chrupala, G., & Foster, J. (2014). DCU-UVT. Word-Level Language Classification with Code-Mixed Data. In First Workshop on Computational Approaches to Code Switching (pp. 127-132). Association for Computational Linguistics (ACL).
    Barman, Utsab ; Wagner, Joachim ; Chrupala, Grzegorz ; Foster, Jennifer. / DCU-UVT. Word-Level Language Classification with Code-Mixed Data. First Workshop on Computational Approaches to Code Switching. Association for Computational Linguistics (ACL), 2014. pp. 127-132
    @inproceedings{5381e8797e60480e81411376ef32a5c6,
    title = "DCU-UVT.: Word-Level Language Classification with Code-Mixed Data",
    abstract = "This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments,we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.",
    author = "Utsab Barman and Joachim Wagner and Grzegorz Chrupala and Jennifer Foster",
    year = "2014",
    language = "English",
    pages = "127--132",
    booktitle = "First Workshop on Computational Approaches to Code Switching",
    publisher = "Association for Computational Linguistics (ACL)",

    }

    Barman, U, Wagner, J, Chrupala, G & Foster, J 2014, DCU-UVT. Word-Level Language Classification with Code-Mixed Data. in First Workshop on Computational Approaches to Code Switching. Association for Computational Linguistics (ACL), pp. 127-132, Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 25/10/14.

    DCU-UVT. Word-Level Language Classification with Code-Mixed Data. / Barman, Utsab; Wagner, Joachim; Chrupala, Grzegorz; Foster, Jennifer.

    First Workshop on Computational Approaches to Code Switching. Association for Computational Linguistics (ACL), 2014. p. 127-132.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientific

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    AU - Wagner, Joachim

    AU - Chrupala, Grzegorz

    AU - Foster, Jennifer

    PY - 2014

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    N2 - This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments,we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.

    AB - This paper describes the DCU-UVT team’s participation in the Language Identification in Code-Switched Data shared task in the Workshop on Computational Approaches to Code Switching. Word-level classification experiments were carried out using a simple dictionary-based method, linear kernel support vector machines (SVMs) with and without contextual clues, and a k-nearest neighbour approach. Based on these experiments,we select our SVM-based system with contextual clues as our final system and present results for the Nepali-English and Spanish-English datasets.

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    EP - 132

    BT - First Workshop on Computational Approaches to Code Switching

    PB - Association for Computational Linguistics (ACL)

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    Barman U, Wagner J, Chrupala G, Foster J. DCU-UVT. Word-Level Language Classification with Code-Mixed Data. In First Workshop on Computational Approaches to Code Switching. Association for Computational Linguistics (ACL). 2014. p. 127-132