中圖分類號：TP391.1文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2024.12.008
引用格式：劉微，李波，楊思瑤. 基于多頭卷積殘差連接的文本數(shù)據(jù)實體識別［J］.網(wǎng)絡安全與數(shù)據(jù)治理，2024，43（12）：54-59.

Text data entity recognition based on muti-head convolution residual connections

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 Robustly 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 Random 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.

Key words : deep learning; named entity recognition; neural networks; data mining