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Abstract

Term proximity scoring models incorporate distance information of query term occurrences and are an established means in information retrieval to improve retrieval quality. The integration of such proximity scoring models into efficient query processing, however, has not been equally well studied. Existing methods make use of precomputed lists of documents where tuples of terms, usually pairs, occur together, usually incurring a huge index size compared to term-only indexes. This paper uses a joint framework for trading off index size and result quality. The framework provides optimization techniques for tuning precomputed indexes towards either maximal result quality or maximal query processing performance under controlled result quality, given an upper bound for the index size.