融合多级语义特征的双通道GAN事件检测方法

Event detection is an important task of information extraction. In recent years, it has been widely used in the fields of knowledge graph construction, information retrieval and intelligence research. For current event detection methods, events within one sentence are often identified as independent individuals, while the correlation among the events within one sentence or document is ignored. Besides, some triggers may trigger different events in different contexts, and the word vectors training in multiple contexts can introduce noise that is not semantically related to the current context. To solve the problems, a double-channel GAN with multi-level semantic correlation was proposed for event detection. Firstly, a multi-level gated attention mechanism was utilized to capture the semantic correlation among sentence-level events and document-level events. And then, a double-channel GAN with self-regulation learning was used to reduce noise and improve accuracy of the representation of event. Finally, some experiments on ACE2005 English corpus were carried out. The results show that, F₁ score can achieve 77%, and the method can effectively obtain semantic correlation among multi-level events, and improve accuracy of context determination.