Re-ranking model based on document clusters

In this paper, we describe a model of information retrieval system that is based on a document reranking method using document clusters. In the ®rst step, we retrieve documents based on the inverted®le method. Next, we analyze the retrieved documents using document clusters, and re-rank them. In this step, we use static clusters and dynamic cluster view. Consequently, we can produce clusters that are tailored to characteristics of the query. We focus on the merits of the inverted-®le method and cluster analysis. In other words, we retrieve documents based on the inverted-®le method and analyze all terms in document based on the cluster analysis. By these two steps, we can get the retrieved results which are made by the consideration of the context of all terms in a document as well as query terms. We will show that our method achieves signi®cant improvements over the method based on similarity search ranking alone. 7 2000 Elsevier Science Ltd. All rights reserved.