Cross-Domain Text Classification

Cross-domain text classification is an important task in natural language processing, aiming to learn an accurate model by utilizing labeled data from multiple source domains to classify new, unlabeled target domains. The core challenge of this task lies in the potential differences in label sets across different domains, thus requiring the model to have strong generalization and adaptability capabilities, enabling it to accurately identify and classify text in various contexts. The application value of cross-domain text classification is extensive, including but not limited to sentiment analysis, topic classification, and intent recognition, which can effectively enhance the efficiency and accuracy of text processing in multiple scenarios.