电子科技大学学报2025，Vol.54Issue(3)：411-423,13.DOI:10.12178/1001-0548.2024106
基于提示学习的ERNIE-BiLSTM-PN通用信息抽取方法研究
Universal information extraction method based on prompt learning with ERNIE-BiLSTM-PN
摘要
Abstract
With the advent of the big data era,information extraction has become a significant research direction in the field of natural language processing.Information extraction involves multiple tasks,including named entity recognition,relation extraction,and event extraction,each typically relying on specialized models to address its specific challenges.This paper proposes a universal information extraction method based on prompt learning(EBP-UIE),enhanced representation through knowledge integration(ERNIE),bi-directional long short-term memory networks(BiLSTM),and pointer networks(PN),aimed at resolving the complexities of information extraction tasks through a unified framework and facilitating cross-task knowledge sharing.The introduction of the ERNIE model enhances deep text understanding and contextual analysis,the application of BiLSTM strengthens the capture of sequential features and the parsing of long-distance dependencies,and the pointer network improves the precise identification of start and end positions of information elements in text.The experimental results show that on named entity recognition,the F1 scores of EBP-UIE on the MSRA and PeopleDaily datasets are respectively 7.12%and 0.53%higher than those of the UIE models;on relation extraction,the F1 score of EBP-UIE on the DuIE dataset exceeded that of the UIE model by 6.84%;And on the event extraction,the F1 score of EBP-UIE outperforms the UIE model by 4.49%and 0.95%in trigger word and argument extraction performance on the DuEE dataset,respectively.关键词
通用信息抽取/深度学习/指针网络/提示学习Key words
universal information extraction/deep learning/pointer network/prompt learning分类
信息技术与安全科学引用本文复制引用
刘万里,雍新有,曹开臣,陈俞舟,刘禄波,蔡世民..基于提示学习的ERNIE-BiLSTM-PN通用信息抽取方法研究[J].电子科技大学学报,2025,54(3):411-423,13.基金项目
国家自然科学基金(T2293771,11975071) （T2293771,11975071）
