网络安全与数据治理2024，Vol.43Issue(12)：54-59,6.DOI:10.19358/j.issn.2097-1788.2024.12.008
基于多头卷积残差连接的文本数据实体识别
Text data entity recognition based on muti-head convolution residual connections
刘微 1李波 1杨思瑶1
作者信息
- 1. 沈阳理工大学 信息科学与工程学院,辽宁 沈阳 110158
- 折叠
摘要
Abstract
To construct a relational database for text data in work reports,and address the problem of extracting useful information entities from unstructured text and feature loss in traditional networks during information extraction,a deep learning-based entity recognition model,which is named RoBERTa-MCR-BiGRU-CRF is proposed.The model firstly uses the pre-trained model Ro-bustly Optimized BERT Pretraining Approach(RoBERTa)as an encoder,feeding the trained word embeddings into the Multi-head Convolutional Residual network(MCR)layer to enrich semantic information.Next,the embeddings are input into a gated recurrent Bidirectional Gated Recurrent Unit(BiGRU)layer to further capture contextual features.Finally,a Conditional Ran-dom Field(CRF)layer is used for decoding and label prediction.Experimental results show that the model achieves an F1 score of 96.64％on the work report dataset,outperforming other comparative models.Additionally,for named entity categories in the data,the F1 score is 3.18％and 2.87％higher than BERT-BiLSTM-CRF and RoBERTa-BiGRU-CRF,respectively.The results demonstrate the model's effectiveness in extracting useful information from unstructured text.关键词
深度学习/命名实体识别/神经网络/数据挖掘Key words
deep learning/named entity recognition/neural networks/data mining分类
信息技术与安全科学引用本文复制引用
刘微,李波,杨思瑶..基于多头卷积残差连接的文本数据实体识别[J].网络安全与数据治理,2024,43(12):54-59,6.
