NDLI: Permutation indexing: fast approximate retrieval from large corpora

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Permutation indexing: fast approximate retrieval from large corpora

Content Provider	ACM Digital Library
Author	Gurevich, Maxim Sarlós, Tamás
Abstract	Inverted indexing is a ubiquitous technique used in retrieval systems including web search. Despite its popularity, it has a drawback - query retrieval time is highly variable and grows with the corpus size. In this work we propose an alternative technique, permutation indexing, where retrieval cost is strictly bounded and has only logarithmic dependence on the corpus size. Our approach is based on two novel techniques: (a) partitioning of the term space into overlapping clusters of terms that frequently co-occur in queries, and (b) a data structure for compactly encoding results of all queries composed of terms in a cluster as continuous sequences of document ids. Then, query results are retrieved by fetching few small chunks of these sequences. There is a price though: our encoding is lossy and thus returns approximate result sets. The fraction of the true results returned, recall, is controlled by the level of redundancy. The more space is allocated for the permutation index the higher is the recall. We analyze permutation indexing both theoretically under simplified document and query models, and empirically on a realistic document and query collections. We show that although permutation indexing can not replace traditional retrieval methods, since high recall cannot be guaranteed on all queries, it covers up to 77% of tail queries and can be used to speed up retrieval for these queries.
Starting Page	1771
Ending Page	1776
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505646
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Precomputation Embedding Inverted index
Content Type	Text
Resource Type	Article

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