Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer Learning

  • Yanna Zhang
  • Jinan Xu
  • Guoyi Miao
  • Yufeng Chen
  • Yujie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)


Recent studies have shown effectiveness in using neural networks for Chinese Word Segmentation (CWS). However, these models, constrained by the domain and size of the training corpus, do not work well in domain adaptation. In this paper, we propose a novel instance-transferring method, which use valuable target domain annotated instances to improve CWS on different domains. Specifically, we introduce semantic similarity computation based on character-based n-gram embedding to select instances. Furthermore, training sentences similar to instances are used to help annotate instances. Experimental results show that our method can effectively boost cross-domain segmentation performance. We achieve state-of-the-art results on Internet literatures datasets, and competitive results to the best reported on micro-blog datasets.


Chinese word segmentation Domain adaptation Instance-transferring Neural network 



The authors are supported by National Nature Science Foundation of China (Contract 61370130 and 61473294), and the Fundamental Research Funds for the Central Universities(2015JBM033), and International Science and Technology Cooperation Program of China under grant No. 2014DFA11350.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yanna Zhang
    • 1
  • Jinan Xu
    • 1
  • Guoyi Miao
    • 1
  • Yufeng Chen
    • 1
  • Yujie Zhang
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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