| |
| Temporal relation classification is an active natural language understanding research field. Partly because of the lack of high-quality datasets, most of the methods proposed in other papers do not use neural network or they only use simple neural network with external knowledge. In this task, we use Convolution Neural Network (CNN) to discover the complex interaction between events and extract time-related keyword information, and we use Long Short-Term Memory (LSTM) to capture temporal context information in sentences, and then connect the two kinds of information for temporal relation classification. We don\'t use any external knowledge, including dependency trees. In the latest MATRES dataset, the performance of our model is better than the state-of-the-art result. |
|
| Keywords:LSTM; CNN; Temporal Relation Classification |
|
