数字图书馆论坛2025，Vol.21Issue(4)：21-32,12.DOI:10.3772/j.issn.1673-2286.2025.04.003
基于机器阅读理解的科技文献三元组抽取模型研究
Triple Extraction Model of Scientific and Technical Literature Based on Machine Reading Comprehension
摘要
Abstract
Scientific and technical literature is an important resource for promoting scientific research and technological progress.However,with the proliferation of literature,researchers are faced with the challenge of quickly obtaining key information from the massive amount of literature.In this paper,we propose an open information extraction model based on machine reading comprehension,namely MMOIE(Multi-Answer Machine-Reading-Comprehension Open Information Extraction),for efficiently extracting triples from scientific and technical literature.The model accurately calculates the critical weights of keywords by combining the SIFRank+model with the ELMo pre-trained language model,and then filters out fact triples containing at least one keyword.The experimental results show that,compared with the existing methods such as ZORE,SpanOIE,MGD-GNN,and TPOIE,the MMOIE model achieves a recall rate of 64.78%and an F1 score of 55.62%for key triple extraction,which significantly improves the efficiency and quality of key information extraction,effectively captures the entity relationships in the literature,and provides strong support for researchers to quickly obtain key information.关键词
科技文献/开放信息/事实三元组/关键三元组/机器阅读理解Key words
Scientific and Technical Literature/Open Information/Fact Triple/Key Triple/Machine Reading Comprehension分类
计算机与自动化引用本文复制引用
王莉军,刘洢颖,郑明,李雪,王鑫月..基于机器阅读理解的科技文献三元组抽取模型研究[J].数字图书馆论坛,2025,21(4):21-32,12.基金项目
本研究得到中信所重点工作项目"面向战略决策的智能情报技术引擎研究及应用"(编号:ZD2025-08)资助. （编号:ZD2025-08）
