<p>In </p><p>recent years, data-driven approaches have become popular for software vulnerability assessment (SVA). However, these approaches need a large amount of labeled SVA data to construct effective SVA models. This process demands security expertise for accurate labeling, incurring significant costs and introducing potential errors. Therefore, collecting the training datasets for SVA can be a challenging task. To effectively alleviate the SVA data labeling cost, we propose an approach SURF, which makes full use of a limited amount of labeled SVA data combined with a large amount of unlabeled SVA data to train the SVA model via semi-supervised learning. Furthermore, SURF incorporates lexical information (i.e., treat the code as plain text) and structural information (i.e., treat the code as the code property graph) as bimodal inputs for the SVA model training, which can further improve the performance of SURF. Through extensive experiments, we evaluated the effectiveness of SURF on a dataset that contains C/C++ vulnerable functions from real-world software projects. The results show that only by labeling 30% of the SVA data, SURF can reach or even exceed the performance of state-of-the-art SVA baselines (such as DeepCVA and Func), even if these supervised baselines use 100% of the labeled SVA data. Furthermore, SURF can also exceed the performance of the state-of-the-art Positive-unlabeled learning baseline PILOT when both are trained on 30% of the labeled SVA data.</p>