Bayesian reinforcement for a probabilistic neural net Part-of-Speech tagger

The present paper introduces a novel stochastic model for Part-of-Speech tagging of natural language texts. While previous statistical approaches, such as Hidden Markov Models, are based on theoretical assumptions that are not always met in natural language, we propose a methodology which incorporates fundamental elements of two distinct machine learning disciplines. We make use of Bayesian knowledge representation to provide a robust classifier, namely a Probabilistic Neural Network one, with additional context information in order to better infer on the correct Part-of-Speech label. As for training material, we make use of minimal linguistic information, i.e. only a small lexicon which contains the words that belong to non-declinable POS categories and closed-class words. Such minimal information is augmented by statistical parameters generated by Bayesian networks learning and the outcome is fed into the Probabilistic Neural Network classifier for the task of Part-of-Speech tagging. Experimental results portray satisfactory performance, in terms of 3.5%-4% error rate.