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This paper addresses two remaining challenges in Chinese word segmentation. The challenge in HLT is to find a robust segmentation method that requires no prior lexical knowledge and no extensive training to adapt to new types of data. The challenge in modelling human cognition and acquisition it to segment words efficiently without using knowledge of wordhood. We propose a radical method of word segmentation to meet both challenges. The most critical concept that we introduce is that Chinese word segmentation is the classification of a string of character-boundaries (CB's) into either word-boundaries (WB's) and non-word-boundaries. In Chinese, CB's are delimited and distributed in between two characters. Hence we can use the distributional properties of CB among the background character strings to predict which CB's are WB's.

Rethinking Chinese word segmentation