Chemical-induced Disease Relation Extraction:Graph Reasoning Method Based on Evidence Focusing

To address the problem of existing methods focusing too much on global information while neglecting a small amount of evidence clues and local mention interactions when mining the interaction between chemicals and diseases,a mention level document-level relation extraction method based on evidence focusing(EF-MUnet) is proposed.This method first models mention features based on context aware strategies and captures local interactions between adjacent mentions using two-dimensional convolution network.Secondly,to avoid irrelevant context interference,two evidence focusing strategies ATT-EF and RL-EF are proposed.The former uses similarity as a measure of evidence clues,while the latter uses reinforcement learning to unsupervised learn the optimal evidence extraction policy with the help of delayed feedback.Finally,U-net networks are used to capture global features at the entity level and fully explore semantic relationships.Experimental results show that compared with existing me-thods,EF-MUnet's F1 score improves by 9.7% on the biomedical dataset CDR,and it has more advantages in extracting inter-sentence relations.In addition,EF-MUnet achieves the highest accuracy of 98.6% on the dataset DMI for extracting interactions between drug and mutation,proving that it is an effective biomedical relation extraction method with good generalization ability.