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- W2893707415 abstract "Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language." @default.
- W2893707415 created "2018-10-05" @default.
- W2893707415 creator A5064336794 @default.
- W2893707415 date "2018-09-21" @default.
- W2893707415 modified "2023-09-27" @default.
- W2893707415 title "Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction." @default.
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