International Journal on Data Science and Technology

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Development of Longest-Match Based Stemmer for Texts of Wolaita Language

Received: 19 May 2018    Accepted: 05 July 2018    Published: 30 July 2018
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Abstract

This research presents design, experiment and development of longest-match based Stemmer for Wolaita texts. The objective of this paper is to conflate the variants of Wolaita text words into its stem with better accuracy, using Longest-Match based approach. To help the researcher how to compile the possible combination of suffixes, the deep analysis of Wolaita word morphology has been made. For data preprocess and implementation, C# programming language is used. After preprocessing, 12789 unique words are reserved to experiment this research. Out of these unique words, 1200 words are randomly selected earlier and kept separate for testing purpose. Then the developed stemmer was tested using Paice’s actual error counting method. The output on that test dataset has showed 91.84% accuracy over actual manually stemmed words. The obtained result shows that the rule based longest match approach is promising for stemming Wolaita language texts.

DOI 10.11648/j.ijdst.20180403.11
Published in International Journal on Data Science and Technology (Volume 4, Issue 3, September 2018)
Page(s) 79-83
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Stemmer, Natural Language Processing, Morphology, Longest-Match

References
[1] Wardhaugh, R. Introduction to Linguistics. New York: McGraw-Hill Book Company, (1977).
[2] Lemma Lessa. “Development of stemming algorithm for Wolaita text.” M. Sc. Thesis, Addis Ababa University, Department of Information Science, Addis Ababa,(2003).
[3] Salton, G. & McGill, N. “Introduction to Modern Information Retrieval”. New York: McGraw-Hill, (1983).
[4] Liddy, E. “Enhanced text retrieval using natural language processing.” Bulletin of the American Society for Information Science, 24, PP. 14-16, (1983).
[5] Schinke, R, et al. "A Stemming Algorithm for Latin Text Databases." In Journal of Documentation. 52(2), PP. 172 – 187, (1996).
[6] Lamberti, Marcello and Sottile, Roberto. ”The Wolaita Language. Koln: Rudiger Koppe Verlag.”, (1997).
[7] Paice C. D. “An Evaluation Method for Stemming Algorithms”. ACM SIGIR Conference on Research and Development in Information Retrieval. 1994, 42-50.
[8] McGregor, W., (2009). Linguistics: An Introduction. London: Continuum International Publishing Group.
[9] Debela T, Ermias. Designing a Rule Based Stemmer for Afaan Oromo Text. International Journal of Computational Linguistics (IJCL), Volume (1): Issue (2), October 2010.
[10] Dawson J. L., 1974: "Suffix removal and word connation," ALLC Bulletin, 2(3), 33-46.
Author Information
  • Department of Computer Science,Wolaita Sodo University, Wolaita, Ethiopia

  • Department of Computer Science and IT, Arba-Minch University, Arba-Minch, Ethiopia

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  • APA Style

    Girma Yohannis Bade, Hussien Seid. (2018). Development of Longest-Match Based Stemmer for Texts of Wolaita Language. International Journal on Data Science and Technology, 4(3), 79-83. https://doi.org/10.11648/j.ijdst.20180403.11

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    ACS Style

    Girma Yohannis Bade; Hussien Seid. Development of Longest-Match Based Stemmer for Texts of Wolaita Language. Int. J. Data Sci. Technol. 2018, 4(3), 79-83. doi: 10.11648/j.ijdst.20180403.11

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    AMA Style

    Girma Yohannis Bade, Hussien Seid. Development of Longest-Match Based Stemmer for Texts of Wolaita Language. Int J Data Sci Technol. 2018;4(3):79-83. doi: 10.11648/j.ijdst.20180403.11

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  • @article{10.11648/j.ijdst.20180403.11,
      author = {Girma Yohannis Bade and Hussien Seid},
      title = {Development of Longest-Match Based Stemmer for Texts of Wolaita Language},
      journal = {International Journal on Data Science and Technology},
      volume = {4},
      number = {3},
      pages = {79-83},
      doi = {10.11648/j.ijdst.20180403.11},
      url = {https://doi.org/10.11648/j.ijdst.20180403.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdst.20180403.11},
      abstract = {This research presents design, experiment and development of longest-match based Stemmer for Wolaita texts. The objective of this paper is to conflate the variants of Wolaita text words into its stem with better accuracy, using Longest-Match based approach. To help the researcher how to compile the possible combination of suffixes, the deep analysis of Wolaita word morphology has been made. For data preprocess and implementation, C# programming language is used. After preprocessing, 12789 unique words are reserved to experiment this research. Out of these unique words, 1200 words are randomly selected earlier and kept separate for testing purpose. Then the developed stemmer was tested using Paice’s actual error counting method. The output on that test dataset has showed 91.84% accuracy over actual manually stemmed words. The obtained result shows that the rule based longest match approach is promising for stemming Wolaita language texts.},
     year = {2018}
    }
    

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    AU  - Hussien Seid
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    JO  - International Journal on Data Science and Technology
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    PB  - Science Publishing Group
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    AB  - This research presents design, experiment and development of longest-match based Stemmer for Wolaita texts. The objective of this paper is to conflate the variants of Wolaita text words into its stem with better accuracy, using Longest-Match based approach. To help the researcher how to compile the possible combination of suffixes, the deep analysis of Wolaita word morphology has been made. For data preprocess and implementation, C# programming language is used. After preprocessing, 12789 unique words are reserved to experiment this research. Out of these unique words, 1200 words are randomly selected earlier and kept separate for testing purpose. Then the developed stemmer was tested using Paice’s actual error counting method. The output on that test dataset has showed 91.84% accuracy over actual manually stemmed words. The obtained result shows that the rule based longest match approach is promising for stemming Wolaita language texts.
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