Event temporal relation computation based on machine learning |
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| Authors: | Dong Wang Ping Zhu Sha-sha Zhu and Wei Liu |
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| Institution: | (1) National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, Japan;(2) Tsuda College, 2-1-1, Tsuda-machi, Kodaira-shi, Tokyo, Japan;(3) Japan Science and Technology Agency (JST), 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, Japan |
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| 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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| Keywords: | event temporal relation machine learning temporal relation computation temporal information processing |
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