Text data entity recognition based on muti-head convolution residual connections
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.
deep learningnamed entity recognitionneural networksdata mining
国家科技期刊平台
NSTL
万方数据
