Impact of Stemming on Telugu Text Classification
Narla Swapna1, Peneti Subhashini2, B Padmaja Rani3
1Dr. Narla Swapna, Department of CSE, CMR College of Engineering & Technology, Hyderabad, India
2Dr. Peneti Subhashini, Department of CSE, MLRIT, Hyderabad, India
3Dr. B Padmaja Rani, Department of CSE, JNTUH, Hyderabad, India
Manuscript received on 14 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 2767-2769 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2338078219/19©BEIESP | DOI: 10.35940/ijrte.B2338.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Text categorization, Information retrieval and document clustering stemming is absolutely necessary especially for morphological rich languages like Indian. The process of stemming is, reducing the inflected or resultant terms to their stem word, root or origin form. However, stemming is a tricky task – particularly for extremely inflected natural languages having a lot of words for the same normalized word form. In Text classification, stemming tries to cut off details like either suffix or prefix of a word and produce basic word. In this paper, we apply various stemming methods on Telugu text classification and ensure the performance of the classifier is effect by stemming. Telugu is suffix oriented language, so we have performed number of experiments on erratically selected Telugu text documents and finally we conceive that the performance of the classifier is improved.
Keywords: Information Retrieval, Stemming, Text Classification, Telugu
Scope of the Article: Classification