Combining Automatic and Manual Index Representations in Probabilistic Retrieval

Results from research in information retrieval suggest that significant improvements in retrieval effectiveness could be obtained by combining results from multiple index representations and query strategies. Recently, an inference network based probabilistic retrieval model has been proposed, which views information retrieval as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the relevance probabilities. In this paper we report a series of experiments we conducted using a specific implementation of this model to study the retrieval effectiveness of combining manaul and automatic index representations in queries and documents. The results indicate that significant benefits in retrieval effectiveness can be obtained through combined representations