A Study of Chinese Lexical Analysis Based on Discriminative Models
Guanglu Sun, Chengjie Sun, Ke Sun, Xiaolong Wang · International Joint Conference on Natural Language Processing · 2008
This paper briefly describes our system in The Fourth SIGHAN Bakeoff. Discriminative models including maximum entropy model and conditional random fields are utilized in Chinese word segmentation and named entity recognition with different tag sets and features. Transformation-based learning model is used in part-of-speech tagging. Evaluation shows that our system achieves the F-scores: 92.64% and 92.73% in NCC Word Segmentation close and open tests, 89.11% in MSRA name entity recognition open test, 91.13% and 91.97% in PKU part-of-speech tagging close and open tests. All the results get medium performances on the bakeoff tracks.