Information Extraction and the Casual User

The task of information extraction (IE) calls for limited understanding of text, limited by the demands of the user and the domain of inquiry -IF. returns specific nstances of the concept orrelation of interest tothe user. Whereas IEsystems have, to date, been oriented towards either system experts (e.g., computational linguists) or domain experts (e.g., professionals searching for information within the field of their profession), the availability oflarge amounts ofon-line t xtual information to the casual user strongly suggests hat echniques oriented towards non-experts are needed. We present a review of current user-involvcmcnt tcchniques in IE, and begin to investigate issues of knowledge r presentation andlearning inthe development of a mixcd-iuitiativc information extraction system. In particular, we discuss some advantages of dividing the knowledge used by the IE system into a query model, a domain model and a corpus model, to assist casual users in interacting with the system. We also advocate flexibility in determining crements forlearning, supporting negotiation between system and user.