@InProceedings{limsopatham-collier:2016:WNUT,
  author    = {Limsopatham, Nut  and  Collier, Nigel},
  title     = {Bidirectional LSTM for Named Entity Recognition in Twitter Messages},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {145--152},
  abstract  = {In this paper, we present our approach for named entity recognition in Twitter
	messages that we used in our participation in the Named Entity Recognition in
	Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text
	(WNUT). The main challenge that we aim to tackle in our participation is the
	short, noisy and colloquial nature of tweets, which makes named entity
	recognition in Twitter message a challenging task. In particular, we
	investigate an approach for dealing with this problem by enabling bidirectional
	long short-term memory (LSTM) to automatically learn orthographic features
	without requiring feature engineering. In comparison with other systems
	participating in the shared task, our system achieved the most effective
	performance on both the `segmentation and categorisation' and the `segmentation
	only' sub-tasks.},
  url       = {http://aclweb.org/anthology/W16-3920}
}