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Abstract

In addition to purely occurrence-based relevance models, term proximity has been frequently used to enhance retrieval quality of keyword-oriented retrieval systems. While there have been approaches on effective scoring functions that incorporate proximity, there has not been much work on algorithms or access methods for their efficient evaluation. This paper presents an efficient evaluation framework including a proximity scoring function integrated within a top-k query engine for text retrieval. We propose precomputed and materialized index structures that boost performance. The increased retrieval effectiveness and efficiency of our framework are demonstrated through extensive experiments on a very large text benchmark collection. In combination with static index pruning for the proximity lists, our algorithm achieves an improvement of two orders of magnitude compared to a term-based top-k evaluation, with a significantly improved result quality.