The Automatic Detection of Language Errors of Binary Adjacent Word Pairs in Automated Essay Scoring
Shili Ge · Computer-assisted Foreign Language Education · 2010
Error is one of the most important features in automated essay scoring(AES) research.The accurate identification and extraction of this feature not only provide support for essay scoring but also offer feedback about language use for writers.The frequencies of binary adjacent word pairs(BAWPs) in large corpus of native English speakers were counted to retrieve the data of BAWPs as the foundation of the research.BAWPs in Chinese college students' English compositions were tagged with the frequencies appearing in native corpus and low frequency BAWPs that are correct were analyzed to construct filter rules of misreport.Combining with these rules and word frequency lists, the highest precision of error identification is close to 69%,which can greatly facilitate AES research.