@InProceedings{xu-jiang-watcharawittayakul:2017:Long,
  author    = {Xu, Mingbin  and  Jiang, Hui  and  Watcharawittayakul, Sedtawut},
  title     = {A Local Detection Approach for Named Entity Recognition and Mention Detection},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1237--1247},
  abstract  = {In this paper, we study a novel approach for named entity recognition (NER) and
	mention detection (MD) in natural language processing. Instead of treating NER
	as a sequence labeling problem, we propose a new local detection approach,
	which relies on the recent fixed-size ordinally forgetting encoding (FOFE)
	method to fully encode each sentence fragment and its left/right contexts into
	a fixed-size representation. Subsequently, a simple feedforward neural network
	(FFNN) is learned to either reject or predict entity label for each individual
	text fragment. The proposed method has been evaluated in several popular NER
	and MD tasks, including CoNLL 2003 NER task and  TAC-KBP2015 and TAC-KBP2016
	Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded
	pretty strong performance in all of these examined tasks. This local detection
	approach has shown many advantages over the traditional sequence labeling
	methods.},
  url       = {http://aclweb.org/anthology/P17-1114}
}