计算机应用与软件2026，Vol.43Issue(2)：242-247,254,7.DOI:10.3969/j.issn.1000-386x.2026.02.031
基于LBC模型的公共资源交易数据命名实体识别研究
LBC:A NAMED ENTITY RECOGNITION FRAMEWORK IN PUBLIC RESOURCE TRANSACTION DATA
沈斐 1李睿 1杨剑凌2
作者信息
- 1. 河南省公共资源交易中心 河南 郑州 450000
- 2. 河南省政务大数据中心 河南 郑州 450000
- 折叠
摘要
Abstract
With the acceleration of digitalization in public resource transactions,data such as tender announcements,bid-winning notices,and bidding records have shown an exponential growth trend.These data contain core information including transaction entities,project attributes,and related entities.How to achieve accurate and generalized entity recognition in such data remains an urgent challenge in this field.This paper proposes a named entity recognition(NER)method for public transaction data based on the LBC(LIFL-BERT-CRF)model.The LIFL module addressed the limitation of insufficient semantic representation in individual characters,BERT provided pre-trained contextual embedding,and the CRF layer optimized global sequence dependencies by considering contextual relationships across the entire sequence.This integrated approach achieved globally optimized decision-making for NER in public resource transactions.The experimental results demonstrate that the proposed model significantly outperforms existing baseline methods in both accuracy and effectiveness.关键词
公共资源交易数据/命名实体识别/BERT/注意力机制Key words
Public transaction data/Named entity recognition/BERT/Attention mechanism分类
信息技术与安全科学引用本文复制引用
沈斐,李睿,杨剑凌..基于LBC模型的公共资源交易数据命名实体识别研究[J].计算机应用与软件,2026,43(2):242-247,254,7.
