Multi-granularity sequential neural network for document-level biomedical relation extraction

Document-level biomedical relation extraction aims to extract the relation between multiple mentions of entities throughout an entire document. However, most methods suffer from longdistance context dependency and complex semantics causing by numerous biomedical entities and inter-sentence relations. In this paper, we propose a multi-granularity sequential network (MGSN) for document-level relation extraction to solve above problems. The proposed method learns to extract the document-level entity relation by the accumulation of document-level information and entity-level information including global and local entity information. In addition, some target entity pairs that reflect target entity relations can be extracted and paid more attention by CNN-based bi-affine structure. Experimental results on three document-level biomedical datasets demonstrate the effectiveness of the proposed model. Our code is available from http://github.com/SCUT-CCNL/MGSN.

Automated summary

This paper proposes a multi-granularity sequential network (MGSN) for document-level relation extraction to address the issues of long-distance context dependency and complex semantics, by learning to extract entity relations at both document-level and entity-level information levels.