Abstract
The purpose of this thesis is to see whether the recent segmentation technique, M-HMM (a specialized Hidden Markov Model), can help the transformation from syllable sequence to character sequence. Basically, this transformation is the task of keyboard input method for entering Chinese character into a computer using phonetic symbols. Unlike the usual word segmentation that segments a character sequence into word sequence, we here group syllables (in phonetic symbols) into word sequence (in group of syllables). Based on BIES, position labels of a Chinese character in a word, M-HMM gives rise to best segmentation candidate that group syllables into words, and then the groups of syllables are transformed into words in characters. This is a two stage approach. For comparison, we also study a one stage approach of M-HMM without using the BIES labels. The finding is that the one stage approach gives better result of 94.60% correctness.