Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information

Abstract

Extracting entity relations from unstructured medical texts is a fundamental task in the field of medical information extraction. In relation extraction, dependency trees contain rich structural information that helps capture the long-range relations between entities. However, many models cannot effectively use dependency information or learn sentence information adequately. In this paper, we propose a relation extraction model based on syntactic dependency structure information. First, the model learns sentence sequence information by Bi-LSTM. Then, the model learns syntactic dependency structure information through graph convolutional networks. Meanwhile, in order to remove irrelevant information from the dependencies, the model adopts a new pruning strategy. Finally, the model adds a multi-head attention mechanism to focus on the entity information in the sentence from multiple aspects. We evaluate the proposed model on a Chinese medical entity relation extraction dataset. Experimental results show that our model can learn dependency relation information better and has higher performance than other baseline models.

Keywords

• dependency information
• graph convolutional networks
• pruning operation
• attention mechanism