| Download | - View accepted manuscript: Feature space selection and combination for native language identification (PDF, 513 KiB)
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| Author | Search for: Goutte, Cyril1; Search for: Léger, Serge1; Search for: Carpuat, Marine1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 8th Workshop on Innovative Use of NLP for Building Educational Applications (BEA8), June 13, 2013, Atlanta, GA |
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| Abstract | We 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. |
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| Publication date | 2013-08-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21270977 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 68cbd15c-c2f6-45b1-8017-ded569f2e8e5 |
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| Record created | 2014-02-20 |
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| Record modified | 2020-06-04 |
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