›› 2011, Vol. 26 ›› Issue (1): 14-24.doi: 10.1007/s11390-011-1107-x

• Special Section on Natural Language Processing • Previous Articles     Next Articles

Chinese New Word Identification: A Latent Discriminative Model with Global Features

Xiao Sun1 (孙晓), De-Gen Huang2 (黄德根), Senior Member, CCF, Hai-Yu Song1 (宋海玉) and Fu-Ji Ren3 (任福继), Member, IEEE   

  1. 1. School of Computer Science and Engineering, Dalian Nationalities University, Dalian 116600, China;
    2. School of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China;
    3. Department of Information Science and Intelligent Systems, Tokushima University, Tokushima 7708506, Japan
  • Received:2009-06-19 Revised:2010-12-14 Online:2011-01-01 Published:2011-01-01
  • About author:Xiao Sun received the M.E. degree in 2004 from the Department of Computer Sciences and Engineering, Dalian University of Technology, Dalian, China. He is now working in School of Computer Science and Engineering, Dalian Nationalities University. He received his doublePh.D. degree from Dalian University of Technology, China, and University of Tokushima in Japan. His research interests include natural language processing, machine translation, Chinese lexical analysis, and machine learning.
    De-Gen Huang was born in 1965. He is a professor in the Dalian University of Technology. His main research interests include natural language processing, machine learning and machine translation. He is now working at the Department of Computer Science and Engineering, Dalian University of Technology. He is now a senior member of CCF, and an associate editor of Int. J. Advanced Intelligence.
    Hai-Yu Song received the B.E. degree in computer and application in 1996, the M.E. degree in computer software and theory in 2003, both from Jilin University, China. Now he is a Ph.D. candidate in computer software and theory at Jilin University, and working in Dalian Nationalities University. His research interests include image analysis and understanding, image retrieval, data mining, and computer graphics.
    Fu-Ji Ren received the B.E. degree in 1982 and M.E. degree in 1985 from the Department of Computer Sciences, Beijing University of Posts and Telecommunications, Beijing, China. He received the Ph.D. degree in 1991 from Faculty of Engineering, Hokkaido University, Japan. He worked at CSK, Japan, where he was a chief researcher of NLP. From 1994 to 2000, he was an associate professor. His research interests include natural language processing, machine translation, artificial intelligence, language understanding and communication.
  • Supported by:

    This work is partially supported by the Doctor Startup Fund of Liaoning Province under Grant No.20101021.

Chinese new words are particularly problematic in Chinese natural language processing. With the fast development of Internet and information explosion, it is impossible to get a complete system lexicon for applications in Chinese natural language processing, as new words out of dictionaries are always being created. The procedure of new words identification and POS tagging are usually separated and the features of lexical information cannot be fully used. A latent discriminative model, which combines the strengths of Latent Dynamic Conditional Random Field (LDCRF) and semi-CRF, is proposed to detect new words together with their POS synchronously regardless of the types of new words from Chinese text without being pre-segmented. Unlike semi-CRF, in proposed latent discriminative model, LDCRF is applied to generate candidate entities, which accelerates the training speed and decreases the computational cost. The complexity of proposed hidden semi-CRF could be further adjusted by tuning the number of hidden variables and the number of candidate entities from the Nbest outputs of LDCRF model. A new-word-generating framework is proposed for model training and testing, under which the definitions and distributions of new words conform to the ones in real text. The global feature called "Global Fragment Features" for new word identification is adopted. We tested our model on the corpus from SIGHAN-6. Experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags with satisfactory results. The proposed model performs competitively with the state-of-the-art models.

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