Advertisement

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)

Abstract

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.

Keywords

Chinese word segmentation Domain adaptation Instance-transferring Neural network 

Notes

Acknowledgments

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.

References

  1. 1.
    Pei, W., Ge, T., Chang, B.: Max-margin tensor neural network for Chinese word segmentation. In: The 52nd Annual Meeting of the Association for Computational Linguistics, pp. 293–303, Baltimore, Maryland (2014)Google Scholar
  2. 2.
    Chen, X., Qiu, X., Zhu, C., Huang, X.: Gated recursive neural network for Chinese word segmentation. In: The 53rd Annual Meeting of the Association for Computer Linguistics, pp. 1744–1753 (2015)Google Scholar
  3. 3.
    Cai, D., Zhao, H.: Neural word segmentation learning for Chinese. In: The 54th Annual Meeting of the Association for Computer Linguistics, pp. 409–420 (2016)Google Scholar
  4. 4.
    Chen, X., Shi, Z., Qiu, X., Huang, X.: Adversarial multi-criteria learning for Chinese word segmentation, pp. 1193–1203 (2017)Google Scholar
  5. 5.
    Qiu, L., Zhang, Y.: Word segmentation for Chinese novels. In: AAAI, pp. 2440–2446 (2015)Google Scholar
  6. 6.
    Liu, Y., Zhang, Y.: Unsupervised domain adaptation for joint segmentation and POS-tagging. In: Proceedings of COLING 2012, Posters, pp. 745–754. The COLING 2012 Organizing Committee (2012)Google Scholar
  7. 7.
    Daume, H., Marcu, D.: Domain adaptation for statistical classifiers. J. Artif. Intell. Res. 26, 101–126 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu, Y., Zhang, Y., Che, W., Liu, T., Wu, F.: Domain adaptation for CRF-based Chinese word segmentation using free annotations. In: EMNLP (2014)Google Scholar
  9. 9.
    Zhang, M., Zhang, Y., Che, W., Liu, T.: Type-supervised domain adaptation for joint segmentation and pos-tagging. In: EACL, pp. 588–597(2014)Google Scholar
  10. 10.
    Jiang, W., Huang, L., Liu, Q., Lü, Y.: A cascaded linear model for joint chinese word segmentation and part-of-speech tagging. In: Meeting of the Association for Computational Linguistics, pp. 897–904, 15–20 June 2008, Columbus, Ohio, USA. DBLP (2008)Google Scholar
  11. 11.
    Xu, J., Ma, S., Zhang, Y., Wei, B., Cai, X., Sun, X.: Transfer deep learning for low-resource chinese word segmentation with a novel neural network. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Yu. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 721–730. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-73618-1_62CrossRefGoogle Scholar
  12. 12.
    Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: EMNLP, pp. 120–128 (2006)Google Scholar
  13. 13.
    Choi, H., Cho, K., Bengio, Y.: Context-dependent word representation for neural machine translation. Comput. Speech Lang. 45, 149–160 (2016)CrossRefGoogle Scholar
  14. 14.
    Qin, L., Zhang, Z., Zhao, H.: Implicit discourse relation recognition with context-aware character-enhanced embeddings. In: The 26th International Conference on Computational Linguistics (COLING), Osaka, Japan, December (2016)Google Scholar
  15. 15.
    Bao, Z., Li, S., Gao, S., Xu, W.: Neural domain adaptation with contextualized character embedding for Chinese word segmentation. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Yu. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 419–430. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-73618-1_35CrossRefGoogle Scholar
  16. 16.
    Zhou, H., Yu, Z., Zhang, Y., Huang, S., Dai, X.: Word-context character embeddings for chinese word segmentation. In: Conference on Empirical Methods in Natural Language Processing, pp. 760–766 (2017)Google Scholar
  17. 17.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition, pp. 260–270 (2016)Google Scholar
  18. 18.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  19. 19.
    Zheng, X., Chen, H., Xu, T.: Deep learning for Chinese word segmentation and POS tagging. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 647–657. Association for Computational Linguistics (2013)Google Scholar
  20. 20.
    Qiu, X., Qian, P., Shi, Z.: Overview of the NLPCC-ICCPOL 2016 shared task: Chinese word segmentation for micro-blog texts. In: Lin, C.-Y., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds.) ICCPOL/NLPCC -2016. LNCS (LNAI), vol. 10102, pp. 901–906. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-50496-4_84CrossRefGoogle Scholar
  21. 21.
    Lin, D., An, X., Zhang, J.: Double-bootstrapping source data selection for instance-based transfer learning. Pattern Recognit. Lett. 34(11), 1279–1285 (2013)CrossRefGoogle Scholar

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

Personalised recommendations