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Computer Science ›› 2019, Vol. 46 ›› Issue (8): 244-248.doi: 10.11896/j.issn.1002-137X.2019.08.040

• Artificial Intelligence • Previous Articles     Next Articles

Event Temporal Relation Classification Method Based on Self-attention Mechanism

ZHANG Yi-jie, LI Pei-feng, ZHU Qiao-ming   

  1. (School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)
    (Province Key Lab of Computer Information Processing Technology of Jiangsu,Suzhou,Jiangsu 215006,China)
  • Received:2018-07-09 Online:2019-08-15 Published:2019-08-15

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Abstract: Classifying temporal relation between events is a significant subsequent study of event extraction.With the development of deep learning,neural network plays a vital role in the task of event temporal relation classification.However,it remains a major challenge for conventional RNNs or CNNs to handle structural information and capture long distance dependence relations.To address this issue,this paper proposed a neural architecture for event temporal relation classification based on self-attention mechanism,which can directly capture relationships between two arbitrary tokens.The classification performance is improved significantly through combing this mechanism with nonlinear layers.The contrast experiments on TimeBank-Dense and Richer Event Description datasets prove that the proposed method outperforms most of the existing neural methods.

Key words: Temporal relation, Deep learning, Self-attention mechanism

CLC Number: 

  • TP391.1
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