An Adaptable Connectionist Text Retrieval System
with Relevance Feedback
Mahmood Azimi,
Department of Electrical Engineering,
Colorado State University
Abstract
This talk introduces a new connenctionist network for large-scale text
retrieval applications. A learning mechanism is proposed to optimally map
the original query using relevance feedback from multiple expert users.
The query mapping not only meets the requirements of the expert users but
also preserves the positions and ranks of other relevant documents. An
updating algorithm is also proposed to incorporate new documents (or delete
the obsolete ones) into the system either one-by-one or in a batch mode
without requiring to retrain the system. The algorithms are successfully
tested on a large database and for a large number of most commonly used
single-term or multi-terms queries.
Note Time Change: This lecture is on Thursday, 4/21,
1:00 pm (not 1:10!), in Weber 117