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Event temporal relation computation based on machine learning

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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.

event temporal relationmachine learningtemporal relation computationtemporal information processing

WANG Dong、ZHU Ping、ZHU Sha-sha、LIU Wei

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

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

国家自然科学基金Basic Scientific Research Project of International Centre for Bamboo RattanShanghai Leading Academic Discipline Project

609750331632009006J50103

2011

10.1007/s11741-011-0773-3

上海大学学报(英文版)
上海大学

上海大学学报(英文版)

影响因子：0.196
ISSN：1007-6417
年,卷(期)：2011.15(5)
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