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Part of Speech Tagging Using Statistical Approach for Nepali Text

Authors: Archit Yajnik


Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: Hidden Markov model, Viterbi algorithm, POS tagging, natural language processing.

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[1] Prajadip Sinha et al. Enhancing the Performance of Part of Speech tagging of Nepali language through Hybrid approach, International Journal of Emerging Technology and Advanced Engineering.2015 Vol 5(5).
[2] Tej Bahadur Shai et al. 2013. Support Vector Machines based Part of Speech Tagging for Nepali Text, International Journal of Computer Applications, May 2013, Vol: 70-No. 24, pp. 0975-8887.
[3] Antony P J et al. 2011.Parts of Speech Tagging for Indian Languages: A Literature Survey, International Journal of Computer Applications, 2011, Vol. 34(8), pp. 0975-8887.
[4] Akshar Bharati, Dipti Misra Sharma, Rajeev Sangal et al., (15th December, 2006), AnnCorra: Annotating Corpora, Guidelines for POS and Chunk Annotation for Indian Languages. Retrieved from
[5] Ben Langmead. (n.d.) Hidden Markov Models. Retrieved from
[6] A part-of-speech tagger for Nepali. (17th May 2006). Retrieved from
[7] PAN Localization.(n.d.). Retrieved from /Outputs%20 Phase%202/CCs/ Nepal /MPP/Papers/2008/Report% 20on% 20Nepali%20Computational%20Grammar.pdf
[8] Chirag Patel et. al., Part-Of- Speech Tagging for Gujarati Using Conditional Random Fields, Proc. Of the IJCNLP-08 Workshop on NLP for Less Privileged Languages, 2008, pp.117-122.