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Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deepdoi:10.3390/s21092893 pmid:33919018 pmcid:PMC8122440 fatcat:sdac7vet3ja5pmtphp76qa4gqu