Semantic context classification by means of fuzzy set theory

Comprehension of semantic meaning is at the heart of modem natural language processing (NLP). Currently, research in statistical NLP has focused primarily on the statistical representation of lexical combinational occurrences. Due to the limitations of current computer technology, however, representing a lexical combination is restricted to a finite length. As such, we focus attention on obtaining approximate but simpler and satisfactory solutions through soft computing techniques; in particular, the fuzzy set theory. It is difficult, however, to apply conventional fuzzy membership functions for general linguistic items. As such, we specifically propose a method of constructing membership functions for linguistic items based on the level of semantic patterns. For testing purpose, the proposed methodology is applied in text classification and the accompanying experimental results are compared with the output provided by a probabilistic based approach.