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International Symposium on Intelligence Computation and Applications

ISICA 2018: Computational Intelligence and Intelligent Systems pp 249–256Cite as

Combining Transformation and Classification for Recognizing Textual Entailment

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 986))

Abstract

This paper introduces an approach combining transformation and classification methods for recognizing textual entailment. In transformation model, directional and undirected inference relations are recognized, and text fragments having such relations in text are replaced by the counterparts in hypothesis. In classification model, a hybrid kernel-based approach is introduced, and three kinds of features are employed for classifying entailment. Experimental results show that the combination approach achieves a better performance in comparison with the single classification system.

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Notes

  1. 1.

    http://dict.baidu.com

  2. 2.

    http://zhishi.me

  3. 3.

    http://www.ltp-cloud.com

  4. 4.

    http://kw.fudan.edu.cn/apis/cnprobase

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Acknowledgements

This work is supported by Natural Science Foundation of Hainan (618MS086), Special innovation project of Guangdong Education Department (2017KTSCX064), Natural Science Foundation of China (61702121) and Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2016ZBKT001, LEC2016ZBKT002).

Author information

Authors and Affiliations

  1. Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou, 510420, China

    Han Ren

  2. Center for Lexicographical Studies, Guangdong University of Foreign Studies, Guangzhou, 510420, China

    Jing Wan

  3. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510420, China

    Xiaomei Chen

Authors
  1. Han Ren
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  2. Jing Wan
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  3. Xiaomei Chen
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Corresponding author

Correspondence to Jing Wan .

Editor information

Editors and Affiliations

  1. School of Information Science and Technology, Jiujiang University, Jiujiang, China

    Hu Peng

  2. School of Information Science and Technology, Jiujiang University, Jiujiang, China

    Changshou Deng

  3. School of Computer, Wuhan University, Wuhan, China

    Zhijian Wu

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Ren, H., Wan, J., Chen, X. (2019). Combining Transformation and Classification for Recognizing Textual Entailment. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_22

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  • DOI: https://doi.org/10.1007/978-981-13-6473-0_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6472-3

  • Online ISBN: 978-981-13-6473-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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