Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey

The problem of tagging in natural language processing is to find a way to tag every word in a text as a particular part of speech, e.g., proper pronoun. POS tagging is a very important preprocessing task for language processing activities. This paper reports about the Part of Speech (POS) taggers pr...

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Published in:International journal of computer applications 2010-01, Vol.6 (5), p.1-9
Main Authors: Kumar, Dinesh, Josan, Gurpreet Singh
Format: Article
Language:English
Subjects:
Accuracy
Indian
Marking
Mathematical models
Maximum entropy
Natural language processing
Preprocessing
Rule based
Speech
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description The problem of tagging in natural language processing is to find a way to tag every word in a text as a particular part of speech, e.g., proper pronoun. POS tagging is a very important preprocessing task for language processing activities. This paper reports about the Part of Speech (POS) taggers proposed for various Indian Languages like Hindi, Punjabi, Malayalam, Bengali and Telugu. Various part of speech tagging approaches like Hidden Markov Model (HMM), Support Vector Model (SVM), Rule based approaches, Maximum Entropy (ME) and Conditional Random Field (CRF) have been used for POS tagging. Accuracy is the prime factor in evaluating any POS tagger so the accuracy of every proposed tagger is also discussed in this paper.
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subjects Accuracy
Indian
Marking
Mathematical models
Maximum entropy
Natural language processing
Preprocessing
Rule based
Speech
title Part of Speech Taggers for Morphologically Rich Indian Languages: A Survey
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