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Stacked Learning for Implicit Discourse Relation Recognition

  • Yang Xu
  • Huibin Ruan
  • Yu HongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)

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.

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Natural Language Processing Lab, School of Computer Science and TechnologySoochow UniversitySuzhouChina

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