TY - GEN
T1 - Correcting grammatical verb errors
AU - Rozovskaya, Alla
AU - Roth, Dan
AU - Srikumar, Vivek
PY - 2014
Y1 - 2014
N2 - Verb errors are some of the most common mistakes made by non-native writers of English but some of the least studied. The reason is that dealing with verb errors requires a new paradigm; essentially all research done on correcting grammatical errors assumes a closed set of triggers - e.g., correcting the use of prepositions or articles - but identifying mistakes in verbs necessitates identifying potentially ambiguous triggers first, and then determining the type of mistake made and correcting it. Moreover, once the verb is identified, modeling verb errors is challenging because verbs fulfill many grammatical functions, resulting in a variety of mistakes. Consequently, the little earlier work done on verb errors assumed that the error type is known in advance. We propose a linguistically-motivated approach to verb error correction that makes use of the notion of verb finiteness to identify triggers and types of mistakes, before using a statistical machine learning approach to correct these mistakes. We show that the linguistically-informed model significantly improves the accuracy of the verb correction approach.
AB - Verb errors are some of the most common mistakes made by non-native writers of English but some of the least studied. The reason is that dealing with verb errors requires a new paradigm; essentially all research done on correcting grammatical errors assumes a closed set of triggers - e.g., correcting the use of prepositions or articles - but identifying mistakes in verbs necessitates identifying potentially ambiguous triggers first, and then determining the type of mistake made and correcting it. Moreover, once the verb is identified, modeling verb errors is challenging because verbs fulfill many grammatical functions, resulting in a variety of mistakes. Consequently, the little earlier work done on verb errors assumed that the error type is known in advance. We propose a linguistically-motivated approach to verb error correction that makes use of the notion of verb finiteness to identify triggers and types of mistakes, before using a statistical machine learning approach to correct these mistakes. We show that the linguistically-informed model significantly improves the accuracy of the verb correction approach.
UR - http://www.scopus.com/inward/record.url?scp=84905676940&partnerID=8YFLogxK
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U2 - 10.3115/v1/e14-1038
DO - 10.3115/v1/e14-1038
M3 - Conference contribution
AN - SCOPUS:84905676940
SN - 9781632663962
T3 - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
SP - 358
EP - 367
BT - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
PB - Association for Computational Linguistics (ACL)
T2 - 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014
Y2 - 26 April 2014 through 30 April 2014
ER -