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Neural recovery machine for chinese dropped pronoun

  • Weinan Zhang
  • Ting Liu
  • Qingyu Yin
  • Yu Zhang
Research Article
  • 2 Downloads

Abstract

Dropped pronouns (DPs) are ubiquitous in prodrop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on two heterogeneous datasets. Further experimental results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs.

Keywords

neural network Chinese dropped pronoun recovery Chinese zero pronoun resolution 

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Notes

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (Grant Nos. 61502120, 61472105, 61772153), Heilongjiang philosophy and social science research project (16TQD03), Young research foundation of Harbin University (HUYF2013-002), the project of university library work committee of Heilongjiang (2013-B-065).

Supplementary material

11704_2018_7136_MOESM1_ESM.ppt (783 kb)
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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research Center for Social Computing and Information Retrieval, School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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