An Effective Framework for Document-level Chemical-induced Disease Relation Extraction via Fine-grained Interaction between Contexts

In recent years, Chemical-induced Disease (CID) relations are the most searched topics by PubMed users worldwide, reflecting its extensive applications in biomedical research and public health field. However, for CID relation extraction, prior methods fail to make full use of the interaction between local and global contexts in biomedical document. To better capture the complex relationships among contexts, we propose an effective framework for document-level CID relation extraction. Specifically, the stacked Hypergraph Aggregation Neural Network (HANN) layers are applied to model effectively the interaction between local and global contexts. Moreover, by constructing CID Relation Heterogeneous Graph, we can capture the different granularities of information and learn better contextualized representations for CID relation extraction. Extensive experiments on a commonly used dataset demonstrate the effectiveness of the proposed method.