Incorporating Temporal Cues and AC-GCN to Improve Temporal Relation Classification

Abstract

Temporal relation classification, an important branch of relation extraction, aims to identify the time sequence among events. Currently, Shortest Dependency Path (SDP) is widely used in various kinds of neural network models to capture the crucial information from sentences. However, while eliminating irrelevant words in event sentences, SDP will miss some useful information, e.g., time expressions. To address the above issue, we propose a neural network method incorporating the temporal cues to AC-GCN (Augmented Contextualized Graph Convolutional Network) to classify temporal relations. Firstly, we introduce the semantic role labeling and heuristic rules to extract the time expressions corresponding to event triggers and other words in SDPs, respectively. Then, the SDP with time expression (i.e., T-SDP) is encoded by a Bi-LSTM with the parameter sharing mechanism and fed into GCN to classify temporal relations. The experimental results on TimeBank-Dense show that our proposed model outperforms all baselines significantly.