Extracting biomedical relations via a multi-head attention based graph convolutional network

Abstract

Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction and knowledge graph construction. However, due to the complex and noisy expressions in biomedical texts, existing traditional neural networks, such as recurrent neural networks and convolutional neural networks, fail to capture syntactic information effectively. In this paper, we introduce a multi-head attention mechanism into graph convolutional networks to extract biomedical relations. In our method, the graph convolutional network is exploited to encode the dependency structure of an input sentence and the multi-head attention mechanism is utilized to alleviate the influence of noisy words. We evaluated our method on the ChemProt corpus and the protein-protein interaction corpus which includes five separate sub-datasets and it achieves F-scores of 67.37% and 84.8% on ChemProt corpus and PPI corpora, respectively. The experimental results suggest that Our model can not only alleviate the influence of noisy words, but also obtain more semantic and syntactic information from dependency graph than previous proposed models.