Research on a Novel Semantic Representation Method Based on Reconstituted Sample Enhancement

Contrastive learning demonstrates superior performance in semantic embedding by capturing relationships between data samples to enhance model representation. However, its effectiveness heavily depends on positive sample construction and the choice of objective functions. Positive samples must be carefully designed to ensure the model can identify meaningful similarities while mitigating the impact of noisy samples. To address this, we propose JCCSE, a novel method that constructs positive samples by splitting, encoding, aggregating, and projecting text. Text is decomposed into segments, encoded to extract semantic content, aggregated to highlight relationships, and finally projected into a semantic space suitable for learning. Additionally, we introduce two supervised loss functions that synergize with the standard contrastive loss, enhancing the discriminative power of the semantic space and improving model performance. Experimental results on MRPC, Quora, and Contrast Clause datasets demonstrate that JCCSE achieves accuracy scores of 89.5%, 92.5%, and 92.1%, with corresponding F1-scores of 91.7%, 91.8%, and 93.3%, respectively. These results indicate that our method enhances the representational quality of semantic embeddings, effectively addresses core challenges in contrastive learning, and lays a solid foundation for further applications in natural language processing.