Part of Speech Tagging of Hindi using Markov Model
Aarti Singh1, Nisheeth Joshi2
1Aarti Singh, Department, of Computer Science, Banasthali Vidyapith (Rajasthan), India.
2Nisheeth Joshi, Department, of Computer Science, Banasthali Vidyapith (Rajasthan), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1723-1726 | Volume-8 Issue-6, April 2019 | Retrieval Number: F4068048619/19©BEIESP
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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: Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. Here, we have used HMM and Trigram model for POS tagging of Hindi. We have trained our tagger on 50000 manually POS tagged sentences. These two algorithms gave us an accuracy of 97.05% and 98.28% respectively for trigram and HMM models.
Keyword: Trigram Model, Hidden Markov Model, Hindi Language, POS Tagging, Sequence Labelling.
Scope of the Article: Advanced Computing Architectures and New Programming Models