Abstract
Classifying temporal relations between events is an important step of understanding natural language, and a significant subsequent study of event extraction. With the development of deep learning, various neural network frameworks have been applied to the task of event temporal relation classification. However, current studies only consider semantic information in local contexts of two events and ignore the syntactic structure information. To solve this problem, this paper proposes a neural architecture combining LSTM and GCN. This method can automatically extract features from word sequences and dependency syntax. A series of experiments on the Timebank-Dense corpus also show the superiority of the model presented in this paper.
This work is supported by Project 61876118 under the National Natural Science Foundation of China, and Key Project 61836007 under the National Natural Science Foundation of China.
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Dai, Q., Kong, F., Dai, Q. (2019). Event Temporal Relation Classification Based on Graph Convolutional Networks. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_35
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DOI: https://doi.org/10.1007/978-3-030-32236-6_35
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