@InProceedings{aguilar-EtAl:2017:WNUT,
  author    = {Aguilar, Gustavo  and  Maharjan, Suraj  and  L\'{o}pez Monroy, Adrian Pastor  and  Solorio, Thamar},
  title     = {A Multi-task Approach for Named Entity Recognition in Social Media Data},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {148--153},
  abstract  = {Named Entity Recognition for social media data is challenging because of its
	inherent noisiness. In addition to improper grammatical structures, it contains
	spelling inconsistencies and numerous informal abbreviations. We propose a
	novel multi-task approach by employing a more general secondary task of Named
	Entity (NE) segmentation together with the primary task of fine-grained NE
	categorization. The multi-task neural network architecture learns higher order
	feature representations from word and character sequences along with basic
	Part-of-Speech tags and gazetteer information. This neural network acts as a
	feature extractor to feed a Conditional Random Fields classifier. We were able
	to obtain the first position in the 3rd Workshop on Noisy User-generated Text
	(WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.},
  url       = {http://www.aclweb.org/anthology/W17-4419}
}