ThetextcatPackage forn -Gram Based Text Categorization inR
Author(s) -
Kurt Hornik,
Patrick Mair,
J. Rauch,
Wilhelm Geiger,
Christian Buchta,
Ingo Feinerer
Publication year - 2013
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v052.i06
Subject(s) - computer science , identification (biology) , categorization , variety (cybernetics) , artificial intelligence , selection (genetic algorithm) , natural language processing , algorithm , programming language , biology , botany
Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods.
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