Medical Entity Relation Extraction Based on Deep Neural Network and Self-attention Mechanism

With the advancement of medical informatization, a large amount of unstructured text data has been accumulated in the medical field.How to mine valuable information from these medical texts is a research hotspot in the field of medical profession and natural language processing.With the development of deep learning, deep neural network is gradually applied to relation extraction task, and "recurrent+CNN" network framework has become the mainstream model in medical entity relation extraction task.However, due to the problems of high entity density and the cross-connection of relationships between entities in medical texts, the "recurrent+CNN" network framework cannot deeply mine the semantic features of medical texts.Based on the "recurrent+CNN" network framework, this paper proposes a Chinese medical entity relation extraction model with multi-channel self-attention mechanism.It includes that BLSTM is used to capture the context information of text sentences, a multi-channel self-attention mechanism is used t