Kannada morpheme segmentation using machine learning

  • Authors

    • Sachi Angle
    • B Ashwath Rao
    • S N. Muralikrishna
    2018-05-29
    https://doi.org/10.14419/ijet.v7i2.31.13395
  • .

  • Abstract

    This paper addresses and targets morpheme segmentation of Kannada words using supervised classification. We have used manually annotated Kannada treebank corpus, which is recently developed by us. Kannada bears resemblance to other Dravidian languages in morphological structure. It is an agglutinative language, hence its words have complex morphological form with each word comprising of a root and an optional set of suffixes. These suffixes carry additional meaning, apart from the root word in a context. This paper discusses the extraction of morphemes of a word by using Support Vector Machines for Classification. Additional features representing the properties of the Kannada words were extracted and the different letters were classified into labels that result in the morphological segmentation of the word. Various  methods for evaluation were considered and an accuracy of 85.97% was achieved.

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  • How to Cite

    Angle, S., Ashwath Rao, B., & N. Muralikrishna, S. (2018). Kannada morpheme segmentation using machine learning. International Journal of Engineering & Technology, 7(2.31), 45-49. https://doi.org/10.14419/ijet.v7i2.31.13395

    Received date: 2018-05-28

    Accepted date: 2018-05-28

    Published date: 2018-05-29