URRel : Joint Entity and Relation Extraction in Chinese Medical Texts via Unified Representation and Reinforcement Learning With Overlapping Relations

ABSTRACT Entity relationship extraction (ERE) plays a crucial role in natural language processing and has great practical significance, especially when applied to electronic medical records (EMRs). Traditional methods usually use hierarchical structures or local decision‐making processes and often perform poorly in complex situations such as Single Entity Overlap (SEO) and Entity Pair Overlap (EPO), resulting in inaccuracies and loss of information. To overcome these limitations, this study introduced URRel, a combination model of entity and relationship extraction that combines unified coding and reinforcement learning (RL) techniques. URRel uses a semantic fusion approach inspired by UniRel to merge sentences and relational descriptions into a single representation, allowing the ability to simultaneously capture entities and their relationships. An interaction matrix was also built to model multiple dependencies between entity pairs and between entities and relationships to improve the model's ability to understand complex relational patterns. URRel views relationship extraction as a sequential decision‐making process and uses an Actor‐Critic RL algorithm to dynamically optimise the extraction strategy and reduce the possibility of decoding errors accumulating over time. Experimental results on the China coronary EMR private dataset (CACMeD) and the publicly available DuIE relational extraction dataset show that URRel significantly outperforms several leading baseline models, yielding F1 scores of 80.35% and 77.87% on DuIE and private dataset CACMeD. This research emphasises the effectiveness of combining unified semantic representation with RL, providing a novel framework for solving complex information extraction challenges.