Abstract
This paper tackles the task of event detection, which involves identifying and categorizing the events. Currently event detection remains a challenging task due to the difficulty at encoding the event semantics in complicate contexts. The core semantics of an event may derive from its trigger and arguments. However, most of previous studies failed to capture the argument semantics in event detection. To address this issue, this paper first provides a rule-based method to predict candidate arguments on the event types of possibilities, and then proposes a recurrent neural network model RNN-ARG with the attention mechanism for event detection to capture meaningful semantic regularities form these predicted candidate arguments. The experimental results on the ACE 2005 English corpus show that our approach achieves competitive results compared with previous work.
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Acknowledgments
The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China under Grant Nos. 61772354, 61773276 and 61472265, and was also supported by the Strategic Pioneer Research Projects of Defense Science and Technology under Grant No. 17-ZLXDXX-02-06-02-04.
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Wu, W., Zhu, X., Tao, J., Li, P. (2018). Event Detection via Recurrent Neural Network and Argument Prediction. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_20
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DOI: https://doi.org/10.1007/978-3-319-99501-4_20
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