Event Detection with Document Structure and Graph Modelling

Abstract

Event detection is the basic task of event extraction. Previous studies usually used independent sentences as basic event detection objects. They cannot effectively identify event triggers which depend on document information. Besides, there are correlations between the sentences and words in the document. Therefore, it is necessary to use document information for event detection. In this study, we propose a graph model for event detection based on document structure. It is used to connect sentences and words in a document. Specifically, we finetune BERT model and use Bi-LSTM to learn the sentences and their context features, and then use GCN to model the document relation graph. The document relation graph is based on the parts of speech of all words in different sentences, which contributes to establishing the triggers-triggers relation and triggers-arguments relation. The experimental results on LitBank show that our proposed model outperforms all baselines significantly and verifies the validity of document information.