With the increasing importance of user data privacy, it is crucial for individuals to understand how companies handle their information. While considerable research has been conducted on automatically identifying privacy-related information in policies, the lack of high-quality annotated training data in this domain remains a significant challenge. Manual annotation of privacy policies is a demanding and time-consuming task that requires domain knowledge. To address this issue, we propose a semi-supervised method, specifically an iterative self-learning approach, to augment the limited training dataset and improve classification performance. Our approach leverages two state-of-the-art models, BERT and XLNet, and involves automatic labelling of data and model retraining with pseudo-labels. We evaluated our approach on the OPP-115 corpora and observed a 10% improvement in the macro F-1 score for BERT, demonstrating the effectiveness of self-learning. This is the first attempt to automatically annotate privacy policies using a self-learning method without requiring additional annotations, offering a promising solution to the challenge of training data scarcity in this domain.