A heuristic information retrieval model on a massively parallel processor

We adapt a competition-based connectionist model to information retrieval. This model, which has been proposed for diagnostic problem solving, treats documents as "disorders" and user information needs as "manifestations", and it uses a competitive activation mechanism which converges to a set of disorders that best explain the given manifestations. Our experimental results using four standard document collections demonstrate the efficiency and the retrieval precision of this model, comparable to or better than that of various information retrieval models reported in the literature. We also propose a parallel implementation of the model on a SIMD machine, MasPar's MP-I. Our experimental results demonstrate the potential to achieve significant speedups.<>