In Chinese lexical analysis, word segmentation is a necessary stage for part-of-speech tagging. In order to fully utilize the information of part-of-speech tagging and reduce the accumulation of errors in the two stages at the word segmentation stage, the best way is to integrate the two stages into one architecture. Based on the undirected graph model, this paper organically unifies word segmentation and part-of-speech tagging in a sequence tagging model. Since deeper dependencies can be used as features, the integrated system achieved a word segmentation accuracy of 97.19% and a part-of-speech tagging accuracy of 95.34% on the 1998 People\'s Daily corpus, which is the best result achieved by similar systems on this corpus.
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