A Statistical Method for Evaluating Performance of Part of Speech Tagger for Gujarati
Pooja M. Bhatt1, Amit Ganatra2
1Pooja M Bhatt, Department of Computer Engineering, CHARUSAT University, Changa, Gujarat, India.
2Dr. Amit Ganatra, Department of Computer Engineering, CHARUSAT University, Changa, Gujarat, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 3899-38-903 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1492078219/19©BEIESP | DOI: 10.35940/ijrte.B1492.078219
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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: Part of Speech Tagging has continually been a difficult mission in the era of Natural Language Processing. This article offers POS tagging for Gujarati textual content the use of Hidden Markov Model. Using Gujarati text annotated corpus for training checking out statistics set are randomly separated. 80% accuracy is given by model. Error analysis in which the mismatches happened is likewise mentioned in element.
Index Terms: BIS Tag Set, Hidden Markov Model, Natural Language Processing, POS Tagging, Statistical Approach.
Scope of the Article: High Performance Concrete