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A Maximum Entropy Approach to Discourse Coherence Modeling

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Natural Language Processing and Chinese Computing (NLPCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9362))

Abstract

This paper introduces a maximum entropy method to Discourse Coherence Modeling (DCM). Different from the state-of-art supervised entity-grid model and unsupervised cohesion-driven model, the model we proposed only takes as input lexicon features, which increases the training speed and decoding speed significantly. We conduct an evaluation on two publicly available benchmark data sets via sentence ordering tasks, and the results confirm the effectiveness of our maximum entropy based approach in DCM.

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Author information

Authors and Affiliations

  1. Machine Intelligence & Translation Lab, Harbin Institute of Technology, Harbin, 150001, China

    Rui Lin, Muyun Yang, Sheng Li & Tiejun Zhao

  2. Microsoft Research, Harbin, China

    Shujie Liu

Authors
  1. Rui Lin
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  2. Muyun Yang
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  3. Shujie Liu
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  4. Sheng Li
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  5. Tiejun Zhao
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Corresponding author

Correspondence to Rui Lin .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Heng Ji

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

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© 2015 Springer International Publishing Switzerland

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Lin, R., Yang, M., Liu, S., Li, S., Zhao, T. (2015). A Maximum Entropy Approach to Discourse Coherence Modeling. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-25207-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25206-3

  • Online ISBN: 978-3-319-25207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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