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Abstract:
In classical large information retrieval systems, the system
responds to a user initiated query with a list of results ranked
by relevance. The users may further refine their query as needed.
This process may result in a lengthy correspondence without
conclusion. We propose an alternative active learning approach,
where the system responds to the initial user's query by
successively probing the user for distinctions at multiple levels
of abstraction. The system's initiated queries are optimized for
speedy recovery and the user is permitted to respond with
multiple selections or may reject the query. The information is
in each case unambiguously incorporated by the system and the
subsequent queries are adjusted to minimize the need for further
exchange. The system's initiated queries are subject to resource
constraints pertaining to the amount of information that can be
presented to the user per iteration.
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