A Document-Centric Approach to Static Index Pruning in Text Retrieval Systems

We present a static index pruning method, to be used in ad-hoc document retrieval tasks, that follows a documentcentric approach to decide whether a posting for a given term should remain in the index or not. The decision is made based on the term's contribution to the document's Kullback-Leibler divergence from the text collection's global language model. Our technique can be used to decrease the size of the index by over 90%, at only a minor decrease in retrieval e#ectiveness. It thus allows us to make the index small enough to fit entirely into the main memory of a single PC, even for large text collections containing millions of documents. This results in great e#ciency gains, superior to those of earlier pruning methods, and an average response time around 20 ms on the GOV2 document collection