@InProceedings{vsspatchigolla-sahu-anand:2017:BioNLP17,
  author    = {V S S Patchigolla, Rahul  and  Sahu, Sunil  and  Anand, Ashish},
  title     = {Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {316--321},
  abstract  = {Biomedical events describe complex interactions between various biomedical
	entities. Event trigger is a word or a phrase which typically signifies the
	occurrence of an event. Event trigger identification is an important first step
	in all event extraction methods. However many of the current approaches either
	rely on complex hand-crafted features or consider features only within a
	window. In this paper we propose a method that takes the advantage of recurrent
	neural network (RNN) to extract higher level features present across the
	sentence. Thus hidden state representation of RNN along with word and entity
	type embedding as features avoid relying on the complex hand-crafted features
	generated using various NLP toolkits. Our experiments have shown to achieve
	state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have
	also performed category-wise analysis of the result and discussed the
	importance of various features in trigger identification task.},
  url       = {http://www.aclweb.org/anthology/W17-2340}
}