Year: 2025
Type: article
Abstract: With the increasing number of web texts, the classification of web texts has become an important task. In this paper, the text word vector representation method is first analyzed, and bidirectional encoder representations from transformers (BERT) are selected to extract the word vector. The bidirectional gated recurrent unit (BiGRU), convolutional neural network (CNN), and attention mechanism are combined to obtain the context and local features of the text, respectively. Experiments were carried out using the THUCNews dataset. The results showed that in the comparison between word-to-vector (Word2vec), Glove, and BERT, the BERT obtained the best classification result. In the classification of different types of text, the average accuracy and F1 value of the BERT-BGCA method reached 0.9521 and 0.9436, respectively, which were superior to other deep learning methods such as TextCNN. The results suggest that the BERT-BGCA method is effective in classifying web texts and can be applied in practice.
Source: Journal of ICT Standardization
Author: Chenwen Zhang
Institution: Hubei University of Arts and Science
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411403771