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POS Tagging of Punjabi Language Using Hidden MarKov Model


Affiliations
1 LPU, Jalandhar, India
2 LPU, Jalndhar, India
3 B.I.S College of Engineering and Technology, Moga – 142001, India
 

POS tagger is the process of assigning a correct tag to each word of the sentence. We attempted to improve the accuracy of existing Punjabi POS tagger. This POS tagger lacks in resolving the ambiguity of compound and complex sentences. A Bi-gram Hidden Markov Model has been used to solve the part of speech tagging problem. An annotated corpus was used for training and estimating of HMM parameter. Maximum likelihood method has been used to estimate the parameter. This HMM approach has been implemented by using Viterby algorithm.
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  • POS Tagging of Punjabi Language Using Hidden MarKov Model

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Authors

Sapna Kanwar
LPU, Jalandhar, India
Mr. Ravishankar
LPU, Jalndhar, India
Sanjeev Kumar Sharma
B.I.S College of Engineering and Technology, Moga – 142001, India

Abstract


POS tagger is the process of assigning a correct tag to each word of the sentence. We attempted to improve the accuracy of existing Punjabi POS tagger. This POS tagger lacks in resolving the ambiguity of compound and complex sentences. A Bi-gram Hidden Markov Model has been used to solve the part of speech tagging problem. An annotated corpus was used for training and estimating of HMM parameter. Maximum likelihood method has been used to estimate the parameter. This HMM approach has been implemented by using Viterby algorithm.