Automatic classification of article errors in L2 written english

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Abstract

This paper presents an approach to the automatic classification of article errors in non-native (L2) English writing, using data chosen from the MELD corpus that was purposely selected to contain only cases with article errors. We report on two experiments on the data: one to assess the performance of different machine learning algorithms in predicting correct article usage, and the other to determine the feasibility of using the MELD data to identify which linguistic properties of the noun phrase containing the article are the most salient with respect to the classification of errors in article usage.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Pages259-264
Number of pages6
StatePublished - 19 Oct 2010
Event23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 - Daytona Beach, FL, United States
Duration: 19 May 201021 May 2010

Publication series

NameProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

Other

Other23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
CountryUnited States
CityDaytona Beach, FL
Period19/05/1021/05/10

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Pradhan, A. M., Varde, A., Peng, J., & Fitzpatrick, E. (2010). Automatic classification of article errors in L2 written english. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 (pp. 259-264). (Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23).