Word Segmentation for the Sequences Emitted from a Word-Valued Source

Word segmentation is the most fundamental and important process for Japanese or Chinese language processing. Because there is no separation between words in these languages, we firstly have to separate the sequence into words. On this problem, it is known that the approach by probabilistic language model is highly efficient, and this is shown practically. On the other hand, recently, a word-valued source has been proposed as a new class of source model for the source coding problem. This model can be supposed to reflect more of the probability structure of natural languages. We may regard Japanese sentence or Chinese sentence as the sequence emitting from a non-prefix-free WVS. In this paper, as the first phase of applying WVS to natural language processing, we formulate a word segmentation problem for the sequence from non-prefix-free WVS. Then, we examine the performance of word segmentation for the models by numerical computations.