Feature space selection and combination for native language identification

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TypeArticle
Proceedings titleProceedings of the 8th Workshop on Innovative Use of NLP for Building Educational Applications (BEA8)
Conference8th Workshop on Innovative Use of NLP for Building Educational Applications (BEA8), June 13, 2013, Atlanta, GA
Pages96100; # of pages: 5
AbstractWe decribe the submissions made by the National Research Council Canada to the Native Language Identification (NLI) shared task. Our submissions rely on a Support Vector Machine classifier, various feature spaces using a variety of lexical, spelling, and syntactic features, and on a simple model combination strategy relying on a majority vote between classifiers. Somewhat surprisingly, a classifier relying on purely lexical features performed very well and proved difficult to outperform significantly using various combinations of feature spaces. However, the combination of multiple predictors allowed to exploit their different strengths and provided a significant boost in performance.
Publication date
PublisherAssociation for Computer Linguistics
LanguageEnglish
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21270977
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Record identifier68cbd15c-c2f6-45b1-8017-ded569f2e8e5
Record created2014-02-20
Record modified2016-05-09
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