Phrase Term Static Index Pruning Based on the Term Cohesiveness

This paper proposes a static index pruning method for phrase queries which is based on the cohesive similarity between terms. The co-occurrence between terms is model by term´s cohesiveness within document. The less relevant terms gets pruned away while assuring that there is no change in the top-k results. The proposed method creates an effective pruned index. This method also considers the term proximity based on the term frequency and the terms informative ness. The experiments were conducted on a number of different standard text collections, and analysis of the results shows promising results and is comparable with the existing static pruning method. It is an advantage of the proposed approach that it can be applies to standard inverted index for phrase queries also.