Few-Shot Joint Extraction of Entity-Relation Triples in the Domain of Ancient Chinese Medical Texts
Year: 2025
Type: article
Abstract: Entity–relation extraction aims to identify structured triples from unstructured text. However, most existing models are evaluated on large, densely annotated corpora; when applied to vertical domains with scarce labeled data, fully su-per-vised models tend to generalize poorly. Existing few-shot learning ap-proaches often simplify the triple extraction task into relation classification with known entities, or overlook the common real-world scenario where a sen-tence con-tains multiple triples. To address these challenges, this paper focuses on ancient Chinese medicine texts and proposes a multi-granularity fused pro-totype net-work. The model fully integrates semantic information between rela-tions and entities during prototype construction to enhance the discriminability among different prototypes. A pointer network is further employed to enable the ex-traction of multiple triples from a single text. Experiments on a self-constructed dataset of ancient Chinese medicine texts demonstrate the ef-fectiveness of the proposed approach.
Source: Economics & Business Management
DOI: 10.63313/ebm.9056
Authors: Chaofan Wang, Jun Pan
Institution: Zhejiang University of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410768437