Semi-Supervised Text Regression

Semi-supervised text regression is a technique in the field of natural language processing that aims to perform text numerical prediction using a small amount of labeled data and a large amount of unlabeled data. This method enhances the generalization ability and prediction accuracy of the model by combining the advantages of supervised and unsupervised learning, thereby demonstrating greater practical value in applications such as sentiment analysis and rating prediction.