Techniques for Lexical Semantics in Hindi Language
Mohd Zeeshan Ansari1, Lubna Khan2
1Mohd Zeeshan Ansari, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
2Lubna Khan, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4075-4080 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36361081219/2019©BEIESP | DOI: 10.35940/ijitee.L3636.1081219
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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: A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is constrained by non-availability of the standard corpora. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the available sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favourable results are achieved.
Keywords: Lexical Semantics, Word Sense Disambiguation, Text Classification, Naïve Bayes.
Scope of the Article: Classification