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Implementation of Stemmer and Lemmatizer for a Low-Resource Language—Kannada

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Proceedings of International Conference on Intelligent Computing, Information and Control Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1272))

Abstract

Stemming and lemmatization are two basic modules used for text normalization in Natural language processing (NLP) which qualifies text, words, and documents for further processing. Stemming is the process of eliminating the affixes from the inflectional word to generate root word. The extracted stem or root word may not be a valid. Lemmatization is also a process of removing the affixes from the word but returning the word in dictionary form which is known as lemma. This lemma will always be meaningful word. Hence, while developing the Lemmatizer semantic knowledge is considered. In this paper, Unsupervised Stemmer and Rule-Based Lemmatizer have been proposed for Kannada. Experimentation is done by building a dataset of 17,825 root words with the help of Kannada dictionary.

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Authors and Affiliations

  1. Department of Computer Science, PES University, Bangalore, India

    G. Trishala & H. R. Mamatha

Authors
  1. G. Trishala
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  2. H. R. Mamatha
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Corresponding author

Correspondence to G. Trishala .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, KGiSL Institute of Technology, Coimbatore, India

    A. Pasumpon Pandian

  2. Department of Business Administration, The Gerald Schwartz School of Business, St. Francis Xavier University, Antigonish, NS, Canada

    Ram Palanisamy

  3. University of Applied Sciences, Aigaleo, Greece

    Klimis Ntalianis

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Trishala, G., Mamatha, H.R. (2021). Implementation of Stemmer and Lemmatizer for a Low-Resource Language—Kannada. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_28

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