21 papers with code • 0 benchmarks • 2 datasets
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.
A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one.
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.