Large-scale computation of distributional similarities for queries

Abstract

We present a large-scale, data-driven approach to computing distributional similarity scores for queries. We contrast this to recent web-based techniques which either require the offline computation of complete phrase vectors, or an expensive on-line interaction with a search engine interface. Independent of the computational advantages of our approach, we show empirically that our technique is more effective at ranking query alternatives that the computationally more expensive technique of using the results from a web search engine.