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(35)　A Word Segmentation Method with Dynamic Adapting to Text Using Inductive Learning
　　　　Proceedings of the First SIGHAN Workshop on Chinese Language Processing, Taipei, Taiwan. pp.113-117, 2002-9

　We have proposed a method of word segmentation for non-segmented language using Inductive Learning. This method uses only surface information of a text, so that it has an advantage that is entirely not dependent on any specific language. In this method, we consider that a character string of appearing frequently in a text has a high possibility as a word. The method predicts unknown words by recursively extracting common parts and different parts from a text, then classifies them into some ranks according to extracted conditions. Those ranks indicate the certainty degrees that common parts and different parts as words. We deal with segmentation ambiguity by using a word candidate in order of the rank, appearing frequency, frequency of correct segmentation and so on. With the proposed method, the results of segmentation can adapt to different users and fields. To evaluate affectivity for Chinese word segmentation and adaptability for different fields, we have done the evaluation experiment with Chinese text of the two fields.