Language Identification With Confidence Limits

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Language Identification With Confidence Limits
A statistical classification algorithm and its application to language identification from noisy input are described. The main innovation is to compute confidence limits on the classification, so that the algorithm terminates when enough evidence to make a clear decision has been made, and so avoiding problems with categories that have similar characteristics. A second application, to genre identification, is briefly examined. The results show that some of the problems of other language identification techniques can be avoided, and illustrate a more important point: that a statistical language process can be used to provide feedback about its own success rate.
David Elworthy
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1999
Where CORR
Authors David Elworthy
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