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 cmail to voicemail using automatic feature extraction, Our agent’s new feature 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 agent’s task. Additionally, features may be defined by vectorized examples or keywords, Experimental results are provided for two email sorting tasks, I<aywords Electronic mail, intelligent agents, machine Iearning, informntion management, feature selection, concept features