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Domain-specific Chinese word segmentation using suffix tree and mutual information

Author

Listed:
  • Daniel Zeng

    (Institute of Automation
    The University of Arizona)

  • Donghua Wei

    (Institute of Automation)

  • Michael Chau

    (The University of Hong Kong)

  • Feiyue Wang

    (Institute of Automation
    The University of Arizona)

Abstract

As the amount of online Chinese contents grows, there is a critical need for effective Chinese word segmentation approaches to facilitate Web computing applications in a range of domains including terrorism informatics. Most existing Chinese word segmentation approaches are either statistics-based or dictionary-based. The pure statistical method has lower precision, while the pure dictionary-based method cannot deal with new words beyond the dictionary. In this paper, we propose a hybrid method that is able to avoid the limitations of both types of approaches. Through the use of suffix tree and mutual information (MI) with the dictionary, our segmenter, called IASeg, achieves high accuracy in word segmentation when domain training is available. It can also identify new words through MI-based token merging and dictionary updating. In addition, with the proposed Improved Bigram method IASeg can process N-grams. To evaluate the performance of our segmenter, we compare it with two well-known systems, the Hylanda segmenter and the ICTCLAS segmenter, using a terrorism-centric corpus and a general corpus. The experiment results show that IASeg performs better than the benchmarks in both precision and recall for the domain-specific corpus and achieves comparable performance for the general corpus.

Suggested Citation

  • Daniel Zeng & Donghua Wei & Michael Chau & Feiyue Wang, 2011. "Domain-specific Chinese word segmentation using suffix tree and mutual information," Information Systems Frontiers, Springer, vol. 13(1), pages 115-125, March.
  • Handle: RePEc:spr:infosf:v:13:y:2011:i:1:d:10.1007_s10796-010-9278-5
    DOI: 10.1007/s10796-010-9278-5
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    References listed on IDEAS

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    1. Zimin Wu & Gwyneth Tseng, 1993. "Chinese text segmentation for text retrieval: Achievements and problems," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 44(9), pages 532-542, October.
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    Cited by:

    1. Aaron W. Baur, 2017. "Harnessing the social web to enhance insights into people’s opinions in business, government and public administration," Information Systems Frontiers, Springer, vol. 19(2), pages 231-251, April.
    2. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    3. Hsinchun Chen & Yilu Zhou & Edna F. Reid & Catherine A. Larson, 2011. "Introduction to special issue on terrorism informatics," Information Systems Frontiers, Springer, vol. 13(1), pages 1-3, March.
    4. Aaron W. Baur, 0. "Harnessing the social web to enhance insights into people’s opinions in business, government and public administration," Information Systems Frontiers, Springer, vol. 0, pages 1-21.
    5. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.

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