Document-Level Joint Biomedical Event Extraction Model Using Hypergraph Convolutional Networks

Abstract

Biomedical event extraction is a fundamental information extraction task, which aims to identify biomedical event triggers and parameters in the text. Although various deep learning and Graph Convolutional Network (GCN) models have been proposed for this task, these models are insufficient to acquire enough local and global context information of documents. To effectively extract joint local and global context information, we propose a joint biomedical event extraction model named BGHGCN, which consists of Bi-directional Long Short-Term Memory (BiLSTM), improved BiAffine Graph Parser (IBGP), GCN and hypergraph convolutional networks (HGCN). Our model employs BiLSTM to learn word sequence features and uses improved BiAffine Graph Parser to enrich dependent syntax features. Afterwards we use GCN to extract local features from IBGP and BiLSTM. Specifically, we introduce HGCN to jointly extract local and global context information with a new fusion mechanism of local feature and incidence matrix, which can effectively extract structural features of hypergraph including node and hyperedge features. Finally, we evaluated our model on two biomedical event datasets MLEE and GE to compare with other baseline models.