Cross-Domain Text Classification Based on BERT Model

Diversity of structure and classification are difficulties for information security data. With the popularization of big data technology, cross-domain text classification becomes increasingly important for the information security domain. In this paper, we propose a new text classification structure based on the BERT model. Firstly, the BERT model is used to generate the text sentence vector, and then we construct the similarity matrix by calculating the cosine similarity. Finally, the k-means and mean-shift clustering are used to extract the data feature structure. Through this structure, clustering operations are performed on the benchmark data set and the actual problems. The text information can be classified, and the effective clustering results can be obtained. At the same time, clustering evaluation indicators are used to verify the performance of the model on these datasets. Experimental results demonstrate the effectiveness of the proposed structure in the two indexes Silhouette coefficient and Calinski-Harabaz.