Chinese Named Entity Recognition Using Role Model

Abstract

This paper presents a stochastic model to tackle the problem of Chinese named entity recognition. In this research, we unify component tokens of named entity and their contexts into a generalized role set, which is like part-of-speech (POS). The probabilities of role emission and transition are acquired after machine learning on a role-labeled data set, which is transformed from a hand-corrected corpus after word segmentation and POS tagging are performed. Given an original string, role Viterbi tagging is employed on tokens segmented in the initial process. Then named entities are identified and classified through maximum matching on the best role sequence. In addition, named entity recognition using role model is incorporated along with the unified class-based bigram model for word segmentation. Thus, named entity candidates can be further selected in the final process of Chinese lexical analysis. Various evaluations conducted using one