开放式中文事件检测研究

doi:10.16088/j.issn.1001-6600.2020.02.007

Abstract

Abstract: In the task of Chinese event detection, there is a problem that domains are independent from each other, and data among domains can not be exchanged. It is necessary to label a large number of data for each domain. Based on previous studies, an open Chinese event detection method based on transfer learning is proposed in this paper. Two association hypotheses of trigger words are studied. The first one is that under the same event type, trigger words are strongly relevant in semantic space with each other. The other one is that among different event types, trigger words are also related with each other, but their relationship are weaker than those under the same event type. Based on the hypotheses, the relationship between candidate words and seed trigger words and the contextual features of candidate words are constructed with the help of external dictionaries. Then,the basic model and the transfer model of event detection are constructed by using convolutional neural network. Finally, only a small amount of tagged data is needed to detect events in the new domain. On ACE2005 Chinese event data set, this method only uses 20% of the data for trigger word recognition,and its effect can surpass the current mainstream method.

Key words: event extraction, transfer learing, seed, convolutional neural network

CLC Number:

TP391

YAN Hao, XU Hongbo, SHEN Yinghan, CHENG Xueqi. Research on Open Chinese Event Detection[J].Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(2): 64-71.

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Research on Open Chinese Event Detection

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