In the area of text mining, Natural Language Processing is an emerging field. As text is an unstructured source of information, to make it a suitable input to an automatic method of information extraction it is usually transformed into a structured format. Part of Speech Tagging is one of the preprocessing steps which perform semantic analysis by assigning one of the parts of speech to the given word. In this paper we had discussed various models of supervised and unsupervised technique shown the comparison of various techniques based on accuracy, and experimentally compared the results obtained in models of Supervised Condition Random Field and Supervised Maximum Entropy model. We had deployed a model of part of speech tagger based on Hidden Markov Model approach and had compare the results with other models. Also we had discussed the problem occurring with supervised part of speech tagging.

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