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• W1985913097 abstract "Native Language Identification (NLI) is the task of identifying the native language of an author of a text written in a second language. Support Vector Machines and Maximum Entropy Learners are the most common methods used to solve this problem, but we consider it from the point-of-view of probabilistic graphical models. We hypothesize that graphical models are well-suited to this task, as they can capture feature inter-dependencies that cannot be exploited by SVMs. Using progressively more connected graphical models, we show that these models out-perform SVMs on reduced feature sets. Furthermore, on full feature sets, even naive Bayes increases accuracy from 82.06% to 83.41% over SVMs on a 5-language classification task." @default.
• W1985913097 created "2016-06-24" @default.
• W1985913097 creator A5006644924 @default.
• W1985913097 creator A5047291957 @default.
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• W1985913097 date "2014-02-01" @default.
• W1985913097 modified "2023-09-27" @default.
• W1985913097 title "Native Language Identification using probabilistic graphical models" @default.
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• W1985913097 doi "https://doi.org/10.1109/eict.2014.6777864" @default.
• W1985913097 hasPublicationYear "2014" @default.
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