@InProceedings{partalas-EtAl:2016:WNUT,
  author    = {Partalas, Ioannis  and  Lopez, C\'{e}dric  and  Derbas, Nadia  and  Kalitvianski, Ruslan},
  title     = {Learning to Search for Recognizing Named Entities in Twitter},
  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     = {171--177},
  abstract  = {We presented in this work our participation in the 2nd Named Entity Recognition
	for Twitter shared task. The task has been cast as a sequence labeling one and
	we employed a learning to search approach in order to tackle it. We also
	leveraged LOD for extracting rich contextual features for the named-entities.
	Our submission
	achieved  F-scores of 46.16 and 60.24 for the classification and the
	segmentation tasks and ranked 2nd and 3rd respectively. The post-analysis
	showed that LOD features improved substantially the performance of our system
	as they counter-balance the lack of context in tweets. The shared task gave us
	the opportunity to test the performance of NER systems in short and noisy
	textual data. The results of the participated systems shows that the task is
	far to be considered as a solved one and  methods with stellar performance in
	normal texts need to be revised.},
  url       = {http://aclweb.org/anthology/W16-3923}
}