@InProceedings{kreuzthaler-EtAl:2016:ClinicalNLP,
  author    = {Kreuzthaler, Markus  and  Oleynik, Michel  and  Avian, Alexander  and  Schulz, Stefan},
  title     = {Unsupervised Abbreviation Detection in Clinical Narratives},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {91--98},
  abstract  = {Clinical narratives in electronic health record systems are a rich resource of
	patient-based information. They constitute an ongoing challenge for natural
	language processing, due to their high compactness and abundance of short
	forms. German medical texts exhibit numerous ad-hoc abbreviations that
	terminate with a period character. The disambiguation of period characters is
	therefore an important task for sentence and abbreviation detection. This task
	is addressed by a combination of co-occurrence information of word types with
	trailing period characters, a large domain dictionary, and a simple rule
	engine, thus merging statistical and dictionary-based disambiguation
	strategies. An F-measure of 0.95 could be reached by using the unsupervised
	approach presented in this paper. The results are promising for a
	domain-independent abbreviation detection strategy, because our approach avoids
	retraining of models or use case specific feature engineering efforts required
	for supervised machine learning approaches.},
  url       = {http://aclweb.org/anthology/W16-4213}
}