Combining Event-Level and Cross-Event Semantic Information for Event-Oriented Relation Classification by SCNN

Previous researches on event relation classification primarily rely on lexical and syntactic features. In this paper, we use a Shallow Convolutional Neural Network (SCNN) to extract event-level and cross-event semantic features for event relation classification. On the one hand, the shallow structure alleviates the over-fitting problem caused by the lack of diverse relation samples. On the other hand, the utilization and combination of event-level and cross-event semantic information help improve relation classification. The experimental results show that our approach outperforms the state of the art.