| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 107 === More than half of English-speaking users are non-native English speakers. For these people, how to quickly and effectively check whether there are grammatical errors in their articles is quite important. Natural Language Processing has always been a very important topic in the field of computer science. Grammatical Error Correction is one of the main research topics. Over the past few years, different approaches to grammatical error correction have been proposed. Each approach has its own advantages and disadvantages. This thesis tries to combine deep learning with mixed N-grams to propose an alternative solution to the problem of grammatical error correction. This solution consists of three types of neural networks: (1) a hybrid N-gram semantic classifier, (2) a hybrid N-gram grammar converter, and (3) a hybrid N-gram grammar converter. This system will first determine whether an English sentence has a mixed N-gram, then check and correct its grammatical error, and finally transform the corrected N-gram back into its corresponding correct English sentence. In this three-stage way, the effect of using the hybrid N-gram to check the English grammar is achieved. Finally, this thesis will use StringNet and CoNLL2013 data sets to verify the performance of the proposed method. The effects of different network structures and data pre-processing methods will be compared and analyzed for three types of neural networks.
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