News Classification
News classification aims to automatically categorize news articles into predefined topics or themes, facilitating tasks like fake news detection, media bias analysis, and personalized news recommendations. Current research emphasizes developing efficient and accurate classification models, often employing transformer-based architectures like BERT and its variants, along with graph neural networks to capture relationships between articles. This field is crucial for managing the ever-increasing volume of online news, improving information retrieval, and enabling more nuanced analyses of media trends and societal impacts.
Papers
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases
T. Y. S. S Santosh, Marcel Perez San Blas, Phillip Kemper, Matthias Grabmair
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases
T. Y. S. S Santosh, Oana Ichim, Matthias Grabmair