1997ACM Transactions on Information SystemsOpen access

Recursive hashing functions for n -grams

Jonathan Cohen

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Abstract

Many indexing, retrieval, and comparison methods are based on counting or cataloguing n -grams in streams of symbols. The fastest method of implementing such operations is through the use of hash tables. Rapid hashing of consecutive n -grams is best done using a recursive hash function, in which the hash value of the current n -gram is drived from the hash value of its predecessor. This article generalizes recursive hash functions found in the literature and proposes new methods offering superior performance. Experimental results demonstrate substantial speed improvement over conventional approaches, while retaining near-ideal hash value distribution.

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What this paper is about

Many indexing, retrieval, and comparison methods are based on counting or cataloguing n -grams in streams of symbols. The fastest method of implementing such operations is through the use of hash tables. Rapid hashing of consecutive n -grams is best done using a recursive hash function, in which the hash value of the current n -gram is drived from the hash value of its predecessor. This article generalizes recursive hash functions found in the literature and proposes new methods offering superior performance. Experimental results demonstrate substantial speed improvement over conventional approaches, while retaining near-ideal hash value distribution.

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Available abstract

Many indexing, retrieval, and comparison methods are based on counting or cataloguing n -grams in streams of symbols. The fastest method of implementing such operations is through the use of hash tables. Rapid hashing of consecutive n -grams is best done using a recursive hash function, in which the hash value of the current n -gram is drived from the hash value of its predecessor. This article generalizes recursive hash functions found in the literature and proposes new methods offering superior performance. Experimental results demonstrate substantial speed improvement over conventional approaches, while retaining near-ideal hash value distribution.

Key concepts: Hash function, Double hashing, Computer science, Universal hashing, Dynamic perfect hashing, Linear hashing, Search engine indexing, Hash table

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