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Stacked Learning for Implicit Discourse Relation Recognition

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Book cover Information Retrieval (CCIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10390))

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Abstract

The existing discourse relation recognition systems have distinctive advantages, such as superior classification models, reliable feature selection, or holding rich training data. This shows the feasibility of making the systems collaborate with each other within a uniform framework. In this paper, we propose a stacked learning based collaborative approach. By the two-level learning, it facilitates the application of the confidence of different systems for the discourse relation determination. Experiments on PDTB show that our method yields promising improvement.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China, No. 61373097, No. 61672368, No. 61672367, No. 61331011. The authors would like to thank the anonymous reviewers for their insightful comments and suggestions. Yu Hong, Professor Associate in Soochow University, is the corresponding author of the paper, whose email address is tianxianer@gmail.com.

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Authors and Affiliations

  1. Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Yang Xu, Huibin Ruan & Yu Hong

Authors
  1. Yang Xu
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  2. Huibin Ruan
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  3. Yu Hong
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Corresponding author

Correspondence to Yu Hong .

Editor information

Editors and Affiliations

  1. Renmin University, Beijing, China

    Jirong Wen

  2. Université de Montréal, Montreal, Canada

    Jianyun Nie

  3. East China University of Science and Technology, Shanghai, China

    Tong Ruan

  4. Tsinghua University, Beijing, China

    Yiqun Liu

  5. Wuhan University, Wuhan, Hubei, China

    Tieyun Qian

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Xu, Y., Ruan, H., Hong, Y. (2017). Stacked Learning for Implicit Discourse Relation Recognition. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-68699-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68698-1

  • Online ISBN: 978-3-319-68699-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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