PRTA:Joint extraction of medical nested entities and overlapping relation via parameter sharing progressive recognition and targeted assignment decoding scheme

Abstract

Nested entities and relationship extraction are two tasks for analysis of electronic medical records. However, most of existing medical information extraction models consider these tasks separately, resulting in a lack of consistency between them. In this paper, we propose a joint medical entity-relation extraction model with progressive recognition and targeted assignment (PRTA). Entities and relations share the information of sequence and word embedding layers in the joint decoding stage. They are trained simultaneously and realize information interaction by updating the shared parameters. Specifically, we design a compound triangle strategy for the nested entity recognition and an adaptive multi-space interactive strategy for relationship extraction. Then, we construct a parameter-shared information space based on semantic continuity to decode entities and relationships. Extensive experiments were conducted on the Private Liver Disease Dataset (PLDD) provided by Beijing Friendship Hospital of Capital Medical University and public datasets (NYT, ACE04 and ACE05). The results show that our method outperforms existing SOTA methods in most indicators, and effectively handles nested entities and overlapping relationships.