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Morpheme Matching Based Text Tokenization for a Scarce Resourced Language

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  • Zobia Rehman
  • Waqas Anwar
  • Usama Ijaz Bajwa
  • Wang Xuan
  • Zhou Chaoying

Abstract

Text tokenization is a fundamental pre-processing step for almost all the information processing applications. This task is nontrivial for the scarce resourced languages such as Urdu, as there is inconsistent use of space between words. In this paper a morpheme matching based approach has been proposed for Urdu text tokenization, along with some other algorithms to solve the additional issues of boundary detection of compound words, affixation, reduplication, names and abbreviations. This study resulted into 97.28% precision, 93.71% recall, and 95.46% F1-measure; while tokenizing a corpus of 57000 words by using a morpheme list with 6400 entries.

Suggested Citation

  • Zobia Rehman & Waqas Anwar & Usama Ijaz Bajwa & Wang Xuan & Zhou Chaoying, 2013. "Morpheme Matching Based Text Tokenization for a Scarce Resourced Language," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0068178
    DOI: 10.1371/journal.pone.0068178
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    References listed on IDEAS

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    1. Christopher C. Yang & K. W. Li, 2005. "A heuristic method based on a statistical approach for Chinese text segmentation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(13), pages 1438-1447, November.
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    Cited by:

    1. Migena Ceyhan & Zeynep Orhan & Dimitrios Karras, 2020. "An Approach for Movie Review Classification in Turkish," European Journal of Engineering and Formal Sciences Articles, European Center for Science Education and Research, vol. 4, May - Aug.
    2. repec:eur:ejfejr:43 is not listed on IDEAS

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