Temporal relation identification of Uyghur event based on Bi-LSTM with attention mechanism; [结合注意力机制的Bi-LSTM维吾尔语事件时序关系识别]

As for the Uyghur event temporal relation identification problem, a model based on bidirectional-long short-term memory (Bi-LSTM) with attention mechanism is proposed. Based on the characteristics of Uyghur language and event temporal relation, 13 features of event internal structural information are extracted. The word embedding is introduced as the Bi-LSTM input to mine the context semantic information implied by a given event sentence. An attention mechanism is established with the event triggers to obtain the event semantic features of the given event sentence. The event internal structural features and the semantic features are combined to be the input of the softmax layer to complete the identification of event temporal relation. The experimental results show that the method can obtain the semantic information of the context and the implicit semantic features of the corresponding event sentence. After fusing the internal structural characteristics of the event, the identification precision rate is 89.42%; the recall rate is 86.70% and the F value for measuring the overall performance of the model is 88.03%,indicating the effectiveness of this method in the identification task of Uyghur event temporal relation. © 2018, Editorial Department of Journal of Southeast University. All right reserved.