@InProceedings{maharana-yetisgen:2017:BioNLP17,
  author    = {Maharana, Adyasha  and  Yetisgen, Meliha},
  title     = {Clinical Event Detection with Hybrid Neural Architecture},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {351--355},
  abstract  = {Event detection from clinical notes has been traditionally solved with rule
	based and statistical natural language processing (NLP) approaches that
	require extensive domain knowledge and feature engineering. In this paper, we
	have explored the feasibility of approaching this task with recurrent neural
	networks, clinical word embeddings and introduced a hybrid architecture to
	improve detection for entities with smaller representation in the dataset. A
	comparative analysis is also done which reveals the complementary behavior of
	neural networks and conditional random fields in clinical entity detection.},
  url       = {http://www.aclweb.org/anthology/W17-2345}
}