@InProceedings{sikdar-gamback:2017:WNUT,
  author    = {Sikdar, Utpal Kumar  and  Gamb\"{a}ck, Bj\"{o}rn},
  title     = {A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {177--181},
  abstract  = {Detecting previously unseen named entities in text is a challenging task. The
	paper describes how three initial classifier models were built using
	Conditional Random Fields (CRFs), Support Vector Machines (SVMs) and a Long
	Short-Term Memory (LSTM) recurrent neural network. The outputs of these three
	classifiers were then used as features to train another CRF classifier working
	as an ensemble. 
	5-fold cross-validation based on training and development data for the emerging
	and rare named entity recognition shared task showed precision, recall and
	F1-score of 66.87%, 46.75% and 54.97%, respectively. For surface form
	evaluation, the CRF ensemble-based system achieved precision, recall and F1
	scores of 65.18%, 45.20% and 53.30%. When applied to unseen test data, the
	model reached 47.92% precision, 31.97% recall and 38.55% F1-score for entity
	level evaluation, with the corresponding surface form evaluation values of
	44.91%, 30.47% and 36.31%.},
  url       = {http://www.aclweb.org/anthology/W17-4424}
}