Research on Confidentiality Management of Language Technology Resources Based on the Combination of BERT and TextRank Algorithms with a Language Technology Vocabulary

This paper proposes a method for classifying the security level of language technology resources by combining the BERT and TextRank algorithms, aimed at improving the efficiency and accuracy of language technology resource classification. Initially, the BERT model is used to generate a language technology vocabulary, and based on this vocabulary, weighted sentence vectors for the target document are created. Subsequently, the BERT word vectors are integrated with the enhanced TextRank algorithm, where the similarity between sentence weights and classification rules determines the security level of the language technology resource. Experimental results demonstrate that the proposed method achieves superior accuracy compared to traditional classification methods. With the increasing significance of language technology in information security, the development of more efficient models for classification will be a crucial direction for future research.