An intelligent tutoring system for deaf learners of written English
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
An unsupervised method for detecting grammatical errors
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
How to detect grammatical errors in a text without parsing it
EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
An Ngram-based reordering model
Computer Speech and Language
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This work presents the evaluation results of a novel technique for word order errors correction, using non native English speakers' corpus. This technique, which is language independent, repairs word order errors in sentences using the probabilities of most typical trigrams and bigrams extracted from a large text corpus such as the British National Corpus (BNC). A good indicator of whether a person really knows a language is the ability to use the appropriate words in a sentence in correct word order. The "scrambled" words in a sentence produce a meaningless sentence. Most languages have a firly fixed word order. For non-native speakers and writers, word order errors are more frequent in English as a Second Language. These errors come from the student if he is translating (thinking in his/her native language and trying to translate it into English). For this reason, the experimentation task involves a test set of 50 sentences translated from Greek to English. The purpose of this experiment is to determine how the system performs on real data, produced by non native English speakers.