关键词: 相似性分析, 数据安全治理, 数据使用合规性, 对比学习, 数据受限

Abstract: Data usage compliance is a key link in data security governance, and its focus is on text traceability and intellectual property protection through text semantic similarity analysis. Aiming at the problem of limited public data resources, a contrastive learning framework is introduced. There are positive and negative sample coupling operators in the existing objective functions commonly used in contrastive learning, resulting in serious gradient attenuation of backpropagation, and there are few batches available for training with small datasets, so it is difficult for the model to converge to the local optimum. This paper proposes contrastive learning text semantic similarity analysis method under small dataset. By calculating the partial derivatives corresponding to the positive and negative samples in the backpropagation of the comparative learning objective function, and eliminating the common factor operator, the gradient decay of the backpropagation is suppressed, and the convergence speed of the model is improved. The experimental results on public datasets show that this method can improve the training efficiency of the model and the effect of text semantic similarity analysis in small datasets.

Key words: similarity analysis;data security governance;data usage compliance, Contrastive Learning, Limited Data