中北大学学报（自然科学版）2025，Vol.46Issue(5)：611-621,11.DOI:10.62756/jnuc.issn.1673-3193.2024.11.0012
基于关系标签语义与全局特征融合的专利实体关系抽取
Patent Entity Relation Extraction Based on the Semantics of Relation Labels and Global Feature Integration
张斌龙 1雷海卫 1李成奇 2智媛1
作者信息
- 1. 中北大学 计算机科学与技术学院,山西 太原 030051
- 2. 陕西飞机工业有限责任公司,陕西 汉中 723200
- 折叠
摘要
Abstract
Patent texts are lengthy and contain heavily overlapping relationships,while traditional relation extraction models treat relations as discrete labels and focus only on local features of the text,failing to fully utilize the latent information,thereby affecting the extraction performance on complex patent texts.To address these issues,this paper proposed a table-filling entity relation extraction model SRL-GFI(Semantics of Relation Labels and Global Feature Integration),based on the OneRel model,which integrated the semantics of relation labels and global features.The SRL-GFI model utilized a BERT pre-trained model to obtain semantic information of relation labels and combined it with tagging scheme to derive a relation-marking joint encoding.This encoding was then matched with embedding of word pairs to obtain the corresponding relevance scores.Subsequently,the global feature integrating module fed back and iterated the intermediate results of the model to integrate global features,thereby performing entity relation extraction.Comparative experiments on multiple datasets show performance improvements of the new model,with F1 scores reaching 79.8%and 55.9%on the patent entity extraction datasets PERD and TFH2020,respectively,representing improvements of 5.0 and 1.8 percentage points over OneRel.The SRL-GFI model proposed in this paper effectively utilizes the semantic information in relation labels,as well as global information in the text and intrinsic connections within relation triplets,making it well-suited for entity relation extraction of complex texts in the patent domain.关键词
实体关系抽取/关系标签语义/全局特征融合/表格填充/专利领域Key words
entity relation extraction/semantics of relation labels/global feature integration/table fill-ing/patent domain分类
信息技术与安全科学引用本文复制引用
张斌龙,雷海卫,李成奇,智媛..基于关系标签语义与全局特征融合的专利实体关系抽取[J].中北大学学报（自然科学版）,2025,46(5):611-621,11.
