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

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Abstract

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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Authors and Affiliations

  1. School of Computer Science and Engineering, Dalian Nationalities University, Dalian, 116600, China

    Xiao Sun & Hai-Yu Song

  2. School of Computer Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    De-Gen Huang (Senior Member, CCF)

  3. Department of Information Science and Intelligent Systems, Tokushima University, Tokushima, 7708506, Japan

    Fu-Ji Ren (Member, IEEE)

Authors
  1. Xiao Sun
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  2. De-Gen Huang
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  3. Hai-Yu Song
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  4. Fu-Ji Ren
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Corresponding author

Correspondence to Xiao Sun.

Additional information

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

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Sun, X., Huang, DG., Song, HY. et al. Chinese New Word Identification: A Latent Discriminative Model with Global Features. J. Comput. Sci. Technol. 26, 14–24 (2011). https://doi.org/10.1007/s11390-011-9411-z

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  • DOI: https://doi.org/10.1007/s11390-011-9411-z

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