Concept features in Re:Agent, an intelligent Email agent

An important issue in the application of machine learning techniques to information management tasks is the nature of features extracted from textual information. We have created an intelligent email agent that can learn actions such as filtering, prioritizing, downloading to palmtops, and forwarding email to voicemail using automatic feature extraction. Our agents newfeature extraction approach is based on first learning concepts present within the mail, then using these concepts as features for learning actions to perform on the messages. What features should be chosen? This paper describes the concept features approach and considers two sources for learning conceptual features: groups defined by the user and groups defined by the agents task. Additionally, features may be defined by vectorized examples or keywords. Experimental results are provided for an email sorting task. Keywords Electronic mail, intelligent agents, machine learning, information management, feature selection...