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dc.contributor.authorGerrit Reinier Bothaen_US
dc.contributor.authorEtienne Barnarden_US
dc.date.accessioned2015-01-30T09:50:45Z
dc.date.available2015-01-30T09:50:45Z
dc.date.issued2012en_US
dc.identifier.citationBotha, G.R. & Barnard, E. 2012. Factors that affect the accuracy of text–based language identification. Computer speech and language, 26:307-320. [http://www.journals.elsevier.com/computer-speech-and-language/]en_US
dc.identifier.issn0885-2308
dc.identifier.issn1095-8363
dc.identifier.urihttp://hdl.handle.net/10394/13136
dc.description.abstractThe classification accuracy of text-based language identification depends on several factors, including the size of the text fragment to be identified, the amount of training data available, the classification features and algorithm employed, and the similarity of the languages to be identified. To date, no systematic study of these factors and their interactions has been published. We therefore investigate the effects of each of these factors and their relations on the performance of text-based language identification. Our study uses n-gram statistics as features for classification. In particular, we compare support vector machines, Naïve Bayesian and difference-in-frequency classifiers on different amounts of input text and various values of n for different amounts of training data. For a fixed value of n the support vector machines generally outperform the other classifiers, but the simpler classifiers are able to handle larger values of n. The additional computational complexity of training the support vector machine classifier may not be justified in light of importance of using a large value of n, except possibly for small sizes of the input window when limited training data is available. Our training and testing corpora consisted of text from the 11 official languages of South Africa. Within these languages distinct language families can be found. We find that it is much more difficult to discriminate languages within languages families than languages in different families. The overall accuracy on short input strings is low for this reason, but for input strings of 100 characters or more there is only a slight confusion within families and accuracies as high as 99.4% are achieved. For the smallest input strings studied here, which consist of 15 characters, the best accuracy achieved is only 83%, but when languages in different families are grouped together, this corresponds to a usable 95.1% accuracy. The relationship between the amount of training data and the accuracy achieved is found to depend on the window size: for the largest window (300 characters) about 400 000 characters are sufficient to achieve close-to-optimal accuracy, whereas improvements in accuracy are found even beyond 1.6 million characters of training data for smaller windows. Our study concludes that the correlation between the factors studied significantly affect classification accuracy; therefore, to assure credible and comparable results, these factors need to be controlled in any text-based language identification task.
dc.description.urihttp://dx.doi.org/10.1016/j.csl.2012.01.004
dc.description.urihttp://ac.els-cdn.com/S0885230812000058/1-s2.0-S0885230812000058-main.pdf?_tid=f4ed669e-68dd-11e4-a01f-00000aab0f27&acdnat=1415626503_97d1740eaadc8b41fe0cedf2a1464609
dc.languageen
dc.publisherElsevier
dc.subjectText-based language identification
dc.subjectn-Gram statistics
dc.subjectNaïve Bayesian classification
dc.subjectDifference-in-frequency classification
dc.subjectSupport vector machine
dc.titleFactors that affect the accuracy of text–based language identificationen_US
dc.typeArticle
dc.contributor.researchID21021287 - Barnard, Etienne


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