Spelling correction is the task of detecting and correcting spelling mistakes.
The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC).
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.