@InProceedings{espinosa-batistanavarro-ananiadou:2016:WNUT,
  author    = {Espinosa, Kurt Junshean  and  Batista-Navarro, Riza Theresa  and  Ananiadou, Sophia},
  title     = {Learning to recognise named entities in tweets by exploiting weakly labelled data},
  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     = {153--163},
  abstract  = {Named entity recognition (NER) in social media (e.g., Twitter) is a challenging
	task due to the noisy nature of text. As part of our participation in the W-NUT
	2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning
	approach using deep neural networks and leverage a knowledge base (i.e.,
	DBpedia) to bootstrap sparse entity types with weakly labelled data. To further
	boost the performance, we employed a more sophisticated tagging scheme and
	applied dropout as a regularisation technique in order to reduce overfitting.
	Even without hand- crafting linguistic features nor leveraging any of the
	W-NUT-provided gazetteers, we obtained robust performance with our approach,
	which ranked third amongst all shared task participants according to the
	official evaluation on a gold standard named entity-annotated corpus of 3,856
	tweets.},
  url       = {http://aclweb.org/anthology/W16-3921}
}