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Event temporal relation computation based on machine learning

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Journal of Shanghai University (English Edition)

Abstract

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event, and it is also the ultimate goal of temporal information processing. However, temporal relation computation based on machine learning requires a lot of hand-marked work, and exploring more features from discourse. A method of two-stage machine learning based on temporal relation computation (TSMLTRC) is proposed in this paper for the shortcomings of current temporal relation computation between two events. The first stage is to get the main temporal attributes of event based on classification learning. The second stage is to compute the event temporal relation in the discourse through employing the result of the first stage as the basic features, and also employing some new linguistic characteristics. Experiments show that, compared with the artificial golden rule, the computational efficiency in the first stage is much higher, and the F1-Score of event temporal relation which is computed through combining multi-features may be increased at 85.8% in the second stage.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, P. R. China

    Dong Wang  (王 东), Sha-sha Zhu  (朱莎莎) & Wei Liu  (刘 炜)

  2. International Centre for Bamboo Rattan, Beijing, 100102, P. R. China

    Ping Zhu  (朱 平)

Authors
  1. Dong Wang  (王 东)
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  2. Ping Zhu  (朱 平)
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  3. Sha-sha Zhu  (朱莎莎)
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  4. Wei Liu  (刘 炜)
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Corresponding author

Correspondence to Wei Liu  (刘 炜).

Additional information

Project supported the National Natural Science Foundation of China (Grant No. 60975033), the Basic Scientific Research Project of International Centre for Bamboo Rattan (Grant No.1632009006), and the Shanghai Leading Academic Discipline Project (Grant No. J50103)

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Wang, D., Zhu, P., Zhu, Ss. et al. Event temporal relation computation based on machine learning. J. Shanghai Univ.(Engl. Ed.) 15, 487–492 (2011). https://doi.org/10.1007/s11741-011-0773-3

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  • DOI: https://doi.org/10.1007/s11741-011-0773-3

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