Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a "common root" assumption to extend DP representations of cross-sentence links. In the final comparison to two stateof-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge and manually annotated attributes of entities (class, tense, polarity, etc.).
Cheng and Miyao REF adopted bidirectional long shortterm memory (Bi-LSTM) along dependency paths for temporal relation classification.
Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths
ACL
Computer Science