The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics

TL;DR

This paper introduces a novel joint entity-relation extraction model tailored for Chinese medical texts with complex semantics, utilizing attention-based modules and interactive fusion to improve extraction accuracy and generalization.

Contribution

The paper proposes a new model with an SEA module and interactive fusion representation for better entity and relation extraction in Chinese medical texts, addressing semantic complexity and task interaction.

Findings

Achieved 96.73% F1-score for entity recognition on CH-DDI.

Attained 78.43% F1-score for relation extraction on CH-DDI.

Demonstrated strong generalization on public datasets.

Abstract

Joint entity-relation extraction is a critical task in transforming unstructured or semi-structured text into triplets, facilitating the construction of large-scale knowledge graphs, and supporting various downstream applications. Despite its importance, research on Chinese text, particularly with complex semantics in specialized domains like medicine, remains limited. To address this gap, we introduce the CH-DDI, a Chinese drug-drug interactions dataset designed to capture the intricacies of medical text. Leveraging the strengths of attention mechanisms in capturing long-range dependencies, we propose the SEA module, which enhances the extraction of complex contextual semantic information, thereby improving entity recognition and relation extraction. Additionally, to address the inefficiencies of existing methods in facilitating information exchange between entity recognition and relation extraction, we present an interactive fusion representation module. This module employs Cross Attention for bidirectional information exchange between the tasks and further refines feature extraction through BiLSTM. Experimental results on both our CH-DDI dataset and public CoNLL04 dataset demonstrate that our model exhibits strong generalization capabilities. On the CH-DDI dataset, our model achieves an F1-score of 96.73% for entity recognition and 78.43% for relation extraction. On the CoNLL04 dataset, it attains an entity recognition precision of 89.54% and a relation extraction accuracy of 71.64%.