An adaptable connectionist text retrieval system with relevance feedback

This paper introduces a new connectionist 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.