@inproceedings{fivez-etal-2017-unsupervised,
    title = "Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings",
    author = "Fivez, Pieter  and
      {\v{S}}uster, Simon  and
      Daelemans, Walter",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-2317/",
    doi = "10.18653/v1/W17-2317",
    pages = "143--148",
    abstract = "We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. We greatly outperform two baseline off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of an optimized noisy channel model, showing that neural embeddings can be successfully exploited to include context-awareness in a spelling correction model."
}