Part of speech tagging with Naïve Bayes methods

In this paper we have focused on the problem of automatic prediction of parts of speech in sentences. We present an experimental framework which includes the analysis and the implementation of methods for part of speech (POS) labeling (tagging). We have tested three methods that predict the POS without current word's context and also three context awareness statistic methods. The main goal of our work was to evaluate the three statistical methods Forward, Backward and Complete Method in order to analyze their applicability in the problem of automatically prediction of the POS. These methods are derived from the classic Naïve Bayes classifier. In our research we have used the WordNet database and a set of benchmarks called the Brown University Standard Corpus of Present - Day American English. The results obtained by the non-context-awareness methods compared to the results obtained by statistical methods are better but not so reliable like the statistical methods.