Abstract: As a branch of information extraction,event temporal relation classification has attracted more and more attention in recent years due to its good auxiliary effect on many natural language processing tasks.At present,the existing neural network approaches lack of consideration for the information interaction between events.To address this issue,this paper proposes a me-thod of event temporal relation classification based on parameter sharing to enhance information interaction between events.This method firstly learns the semantic information and context information of sentences through gated convolutional neural networks (GCNN),and incorporates them into the shortest dependency path sequence as input.Then,it uses Bidirectional long short-term memory network (Bi-LSTM) to encode the input and capture its semantic representation.In addition,it enhances the information interaction between event pairs by parameter sharing.Finally,the obtained semantic representation is input into the fully connec-ted layer,and the softmax function is used for classification prediction.Experimental results on TimeBank-Dense show that the proposed method outperforms most of the existing neural network methods in classification accuracy.

Key words: Bidirectional long and short-term memory network, Information interaction, Sentence representation, Shortest dependency path, Temporal relation classification