Document representation in probabilistic models of information retrieval

Abstract Probabilistic models of retrieval have provided insights into the document retrieval process and contain the basis for very effective search strategies. A major limitation of these models is that they assume that documents are represented by binary index terms. In many cases the index terms will be assigned weights, such as within‐document frequency weights, which are derived from the content of the documents by the indexing process. These weights, which are referred to here as term significance weights, indicate the relative importance of the terms in individual documents. This article describes how retrieval models which use either independence or dependence assumptions can be extended to include document representatives containing term significance weights. Comparison with other research indicates that search strategies based on models modified in this way can further improve the effectiveness of document retrieval systems.