Chinese segmentation and new word detection using conditional random fields

  • Authors:
  • Fuchun Peng;Fangfang Feng;Andrew McCallum

  • Affiliations:
  • University of Massachusetts Amherst, Amherst, MA;University of Massachusetts Amherst, Amherst, MA;University of Massachusetts Amherst, Amherst, MA

  • Venue:
  • COLING '04 Proceedings of the 20th international conference on Computational Linguistics
  • Year:
  • 2004

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Abstract

Chinese word segmentation is a difficult, important and widely-studied sequence modeling problem. This paper demonstrates the ability of linear-chain conditional random fields (CRFs) to perform robust and accurate Chinese word segmentation by providing a principled framework that easily supports the integration of domain knowledge in the form of multiple lexicons of characters and words. We also present a probabilistic new word detection method, which further improves performance. Our system is evaluated on four datasets used in a recent comprehensive Chinese word segmentation competition. State-of-the-art performance is obtained.