ACUT: An Associative Classifier Approach to Unknown Word POS Tagging

The focus of this article is unknown word Part-of-Speech (POS) tagging. POS tagging which is one the fundamental requirements for intelligent text processing based on texts language. Therefore, this article firstly aims to provide a POS tagger with high accuracy for Persian language. The technique which is proposed by this article for handling unknown words is using a combination of a type of associative classifier along with a Hidden Markov Models (HMM) algorithm. Associative classification is a new classification approach integrating association mining and classification. The associative classifier used in this study is a type of associative classifiers that is innovated by this research. This kind of classifier not only uses sequence probability but also uses the CBA classifier. Based on the experimental results, the proposed algorithm can increase the accuracy of Persian unknown word POS tagging to 81.8%. The total accuracy of proposed tagger is 98% and its sentence accuracy is 63.1%.