计算机工程与科学2024，Vol.46Issue(8)：1473-1481,9.DOI:10.3969/j.issn.1007-130X.2024.08.016
一种基于多特征融合嵌入的中文命名实体识别模型研究
A Chinese named entity recognition model based on multi-feature fusion embedding
摘要
Abstract
In order to solve the problems of differences in Chinese glyphs and blurred boundaries of Chinese words,a Chinese named entity recognition model based on multi-feature fusion embedding is proposed.On the basis of extracting semantic features,glyph features are captured based on convolu-tional neural network and multi-headed self-attention mechanism,word features are obtained with refer-ence to the word vector embedding table,and the bidirectional long short-term memory neural network is used to learn the context representation of long distance.Finally the constraint conditions in sentence sequence labels are learned by combining the conditional random field to realize Chinese named entity recognition.The Fl values on the Resume,Weibo and People Daily datasets reach 96.66％,70.84％and 96.15％,respectively,which proves that the proposed model effectively improves the performance of Chinese named entity recognition tasks.关键词
命名实体识别/特征融合/多头自注意力机制Key words
named entity recognition/feature fusion/multi-headed self-attention mechanism分类
信息技术与安全科学引用本文复制引用
刘晓华,徐茹枝,杨成月..一种基于多特征融合嵌入的中文命名实体识别模型研究[J].计算机工程与科学,2024,46(8):1473-1481,9.基金项目
国家自然科学基金(61972148) （61972148）
