Author(s)

Wenwen Zhao ¹

Affiliation(s)

¹ Xinjiang Hetian College, Hotan, Xinjiang, China

Corresponding Author

Wenwen Zhao

ABSTRACT

In today's information explosion, automatic text categorization has become an important task in text processing, which not only helps users to quickly and effectively distinguish and manage massive amounts of information, but also greatly facilitates the development of fields such as opinion analysis and sentiment recognition. It is worth mentioning that the emergence of deep learning models has brought unprecedented changes to automatic text categorization, especially with the promotion of convolutional neural networks (CNN), long short-term memory networks (LSTM) and BERT models, the accuracy of text categorization has been significantly improved. However, with the advancement of technology, traditional models still face many challenges when dealing with complex text tasks, such as the lack of domain knowledge and insufficient comprehension of long texts. Therefore, automatic text categorization methods based on knowledge enhancement have emerged to make up for the deficiencies of traditional models and improve the classification performance and robustness by introducing an external knowledge base. In this paper, we will explore the application of different knowledge enhancement strategies in text categorization, focusing on the structure of the model framework and its functions of each layer, aiming to provide researchers with new ideas and technical support.

KEYWORDS

Knowledge enhancement; automatic text categorization

CITE THIS PAPER

Wenwen Zhao, Research on Automatic Text Categorization Method Based on Knowledge Enhancement. Automation and Machine Learning (2025) Vol. 6: 1-11. DOI: http://dx.doi.org/10.23977/autml.2025.060101.

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