23 July 2010
Ranking search results is a fundamental problem in information retrieval. In this paper we explore whether the use
of proximity and phrase information can improve web retrieval accuracy. We build on existing research by incorporating novel ranking features based on ﬂexible proximity
terms with recent state-of-the-art machine learning ranking
models. We introduce a method of determining the goodness of a set of proximity terms that takes advantage of the
structured nature of web documents, document metadata,
and phrasal information from search engine user query logs.
We perform experiments on a large real-world Web data
collection and show that using the goodness score of ﬂexible
proximity terms can improve ranking accuracy over state-ofthe-art ranking methods by as much as 13%. We also show
that we can improve accuracy on the hardest queries by as
much as 9% relative to state-of-the-art approaches.
Venue : N/A
External Link: http://people.cs.umass.edu/~pallika/publications/fp728-svore.pdf