计算机与数字工程2025，Vol.53Issue(11)：3162-3167,6.DOI:10.3969/j.issn.1672-9722.2025.11.030
多级上下文信息增强的实体识别模型
Entity Recognition Model Based on Multi-level Contextual Information Enhanced
摘要
Abstract
Contextual word embeddings can bring more semantic and grammatical information for named entity recognition(NER),but the level of information only stays between words in one sentence,lacking the usage of morpheme level information of words and the overall theme information of the article.Addressing this issue,one named entity recognition model is proposed with contextual embeddings,in which multi-level context information is carried as a part of an input.In the MPNet training process,doc-ument context word embeddings is obtained via passing a sentence with its surrounding context.The contextual word embeddings and string embeddings are combined as the input of sequence labeling module.Experimental results show that the final model achieves the state-of-the-art results on the datasets,such as CoNLL-2003,CoNLL++and OntoNotes 5.0.关键词
自然语言处理/命名实体识别/预训练模型/双向长短期记忆网络/条件随机场Key words
natural language processing/named entity recognition/pre-trained models/bi-directional long short-term memory/conditional random field分类
信息技术与安全科学引用本文复制引用
王谭,陈金广,马丽丽..多级上下文信息增强的实体识别模型[J].计算机与数字工程,2025,53(11):3162-3167,6.基金项目
陕西省自然科学基础研究计划项目(编号:2023-JC-YB-568) （编号:2023-JC-YB-568）
陕西省教育厅科研计划项目(编号:22JP028)资助. （编号:22JP028）
