Abstract
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 fairly 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.
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Athanaselis, T., Bakamidis, S., Dologlou, I. (2011). Performance Evaluation of a Novel Technique for Word Order Errors Correction Applied to Non Native English Speakers’ Corpus. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19437-5_33
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DOI: https://doi.org/10.1007/978-3-642-19437-5_33
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