@InProceedings{mehryary-EtAl:2017:BioNLP17,
  author    = {Mehryary, Farrokh  and  Hakala, Kai  and  Kaewphan, Suwisa  and  Bj\"{o}rne, Jari  and  Salakoski, Tapio  and  Ginter, Filip},
  title     = {End-to-End System for Bacteria Habitat Extraction},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {80--90},
  abstract  = {We introduce an end-to-end system capable of named-entity detection,
	normalization and relation extraction for extracting information about bacteria
	and their habitats from biomedical literature. Our system is based on deep
	learning, CRF classifiers and vector space models. We train and evaluate the
	system on the BioNLP 2016 Shared Task Bacteria Biotope data. The official
	evaluation shows that the joint performance of our entity detection and
	relation extraction models outperforms the winning team of the Shared Task by
	19pp on F1-score, establishing a new top score for the task. We also achieve
	state-of-the-art results in the normalization task. Our system is open source
	and freely available at https://github.com/TurkuNLP/BHE.},
  url       = {http://www.aclweb.org/anthology/W17-2310}
}