@InProceedings{fivez-suster-daelemans:2017:BioNLP17,
  author    = {Fivez, Pieter  and  Suster, Simon  and  Daelemans, Walter},
  title     = {Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2317}
}