Abstract The recent growth of the Internet has left many users awash in a sea of information. This development has spawned the need for intelligent filtering systems. This paper describes work implemented in the INFOS (Intelligent News Filtering Organizational System) project that is designed to reduce the user's search burden by automatically categorizing data as relevant or irrelevant based upon user interests. These predictions are learned automatically based upon features taken from input articles and collaborative features derived from other users. The filtering is performed by a hybrid technique that combines elements of a keyword-based hill climbing method, knowledge-based conceptual representation via WordNet, and partial parsing via index patterns. The hybrid system integrating all these approaches combines the benefits of each while maintaining robustness and scalability.