A minimum cluster-based trigram statistical model for Thai syllabification

Abstract

Syllabification is a process of extracting syllables from a word. Problems of syllabification are majorly caused from unknown and ambiguous words. This research aims to resolve these problems in Thai language by exploiting relationships among characters in the word. A character clustering scheme is proposed to generate units smaller than a syllable, called Thai Minimum Clusters (TMCs), from a word. TMCs are then merged into syllables using a trigram statistical model. Experimental evaluations are performed to assess the effectiveness of the proposed technique on a standard data set of 77,303 words. The results show that the technique yields 97.61% accuracy. © 2011 Springer-Verlag.