计算机与现代化Issue(9)：91-94,4.DOI:10.3969/j.issn.1006-2475.2024.09.015
基于BERT-BiLSTM-CRF党建领域命名实体识别
Named Entity Recognition in Field of Party Building Based on BERT-BiLSTM-CRF
摘要
Abstract
When constructing a knowledge graph in the field of party building,the traditional named entity recognition(NER)methods often suffer from unclear entity boundaries and polysemy of entity terms,which lead to low recognition accuracy and effi-ciency.To address these issues,this paper proposes a BERT-BiLSTM-CRF entity recognition model that integrates tree-like probability and a domain dictionary.The model involves embedding the domain dictionary into BERT for text vectorization,uti-lizes BiLSTM to acquire contextual semantic features,and applies tree-like probability to the transition probability calculation in the CRF layer to enhance word segmentation accuracy.The experimental results on the MSRA and self-constructed corpora,compared with the baseline model,show that the proposed model achieves better performance in terms of F1-score,recall,and precision.关键词
BERT-BiLSTM-CRF模型/树形概率/领域词典/命名实体识别Key words
BERT-BiLSTM-CRF model/tree-like probability/domain dictionary/name entity recognition分类
信息技术与安全科学引用本文复制引用
赵盾,佘学兵,邬昌兴..基于BERT-BiLSTM-CRF党建领域命名实体识别[J].计算机与现代化,2024,(9):91-94,4.基金项目
国家自然科学基金地区科学基金资助项目(62266017) （62266017）
江西省教育厅科技项目(GJJ2202608) （GJJ2202608）
