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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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Named Entity Recognition is the task of identifying and classifying Named Entities in the given text. In this paper evaluation of Named Entity Recognition in Punjabi language has been performed using context word feature. Words preceding and succeeding the target word are very helpful in determining its category. In this work context word feature of word window 7, 5 and 3 have been used.