@InProceedings{salloum-EtAl:2017:BioNLP171,
  author    = {Salloum, Wael  and  Finley, Greg  and  Edwards, Erik  and  Miller, Mark  and  Suendermann-Oeft, David},
  title     = {Deep Learning for Punctuation Restoration in Medical Reports},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {159--164},
  abstract  = {In clinical dictation, speakers try to be as concise as possible to save time,
	often resulting in utterances without explicit punctuation commands.  Since the
	end product of a dictated report, e.g. an out-patient letter, does require
	correct orthography, including exact punctuation, the latter need to be
	restored, preferably by automated means.  This paper describes a method for
	punctuation restoration based on a state-of-the-art stack of NLP and machine
	learning techniques including B-RNNs with an attention mechanism and late
	fusion, as well as a feature extraction technique tailored to the processing of
	medical terminology using a novel vocabulary reduction model.  To the best of
	our knowledge, the resulting performance is superior to that reported in prior
	art on similar tasks.},
  url       = {http://www.aclweb.org/anthology/W17-2319}
}