TY - GEN
T1 - Automatic classification of article errors in L2 written english
AU - Pradhan, Aliva M.
AU - Varde, Aparna S.
AU - Peng, Jing
AU - Fitzpatrick, Eileen M.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77957883366&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77957883366
SN - 9781577354475
T3 - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
SP - 259
EP - 264
BT - Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
T2 - 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Y2 - 19 May 2010 through 21 May 2010
ER -