Abstract:
The objective of this study is to address the challenge of context-sensitive spelling errors in English papers. Spelling mistakes in English can be categorized into non-word errors and context-sensitive errors. Correcting context-sensitive spelling errors is more challenging as it requires understanding the relationship between the word to be corrected and its surrounding context. Non-word errors, on the other hand, can be easily resolved by comparing words with a dictionary. In any field that deals with text data, spelling mistakes are considered noise, and preprocessing techniques are needed to reduce this issue. Context-sensitive spelling errors encompass errors across word boundaries, typographical errors, grammatical errors, and homophone errors. This study primarily focuses on typographical mistakes, which arise from incorrect spacing between words due to pressing the wrong keys on a keyboard. To tackle the context-sensitive spelling mistake problem, this study utilizes a deep learning approach, which incorporates word embedding information, contextual embedding information, an auto-regressive (AR) language model, and an auto-encoding (AE) language model for correction. Through comprehensive testing, the AE language model-based technique demonstrated the best correction performance.
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