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Cited by 534

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Gauging Similarity with n -Grams: Language-Independent Categorization of Text

Damashek, Marc·1995·Science

Abstract

A language-independent means of gauging topical similarity in unrestricted text is described. The method combines information derived from n -grams (consecutive sequences of n characters) with a simple vector-space technique that makes sorting, categorization, and retrieval feasible in a large multilingual collection of documents. No prior information about document content or language is required. Context, as it applies to document similarity, can be accommodated by a well-defined procedure. When an existing document is used as an exemplar, the completeness and accuracy with which topically related documents are retrieved is comparable to that of the best existing systems. The results of a formal evaluation are discussed, and examples are given using documents in English and Japanese.

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Metadata

Authors
Damashek, M.
Venue
Science
Published
1995-02-10
Volume
267
Issue
5199
Pages
843-848
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2078396547
Cited by
534
References
1
FWCI
12.93
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Gauging Similarity with n -Grams: Language-Independent Categorization of Text. https://app.cochat.ai/papers/10.1126/science.267.5199.843
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Damashek, M. (1995). Gauging Similarity with n -Grams: Language-Independent Categorization of Text. Science. https://doi.org/10.1126/science.267.5199.843

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