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

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  • 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). http://www.aclweb.org/anthology/W/W14/W14-39.pdf#page=135