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. (authors' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3985 |
| Date | 02 1900 |
| Creators | Feinerer, Ingo, Buchta, Christian, Geiger, Wilhelm, Rauch, Johannes, Mair, Patrick, Hornik, Kurt |
| Publisher | American Statistical Association |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://www.jstatsoft.org/v52/i06/paper, http://epub.wu.ac.at/3985/ |
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