@InProceedings{gerguis-salama-elkharashi:2016:WNUT,
  author    = {Gerguis, Michel Naim  and  Salama, Cherif  and  El-Kharashi, M. Watheq},
  title     = {ASU: An Experimental Study on Applying Deep Learning in Twitter Named Entity Recognition.},
  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     = {188--196},
  abstract  = {This paper describes the ASU system submitted in the COLING W-NUT 2016 Twitter
	Named Entity Recognition (NER) task.
	We present an experimental study on applying deep learning to extracting named
	entities (NEs) from tweets.
	We built two Long Short-Term Memory (LSTM) models for the task.
	The first model was built to extract named entities without types while the
	second model was built to extract and then classify them into 10 fine-grained
	entity classes.
	In this effort, we show detailed experimentation results on the effectiveness
	of word embeddings, brown clusters, part-of-speech (POS) tags, shape features,
	gazetteers, and local context for the tweet input vector representation to the
	LSTM model.
	Also, we present a set of experiments, to better design the network parameters
	for the Twitter NER task.
	Our system was ranked the fifth out of ten participants with a final f1-score
	for the typed classes of 39% and 55% for the non typed ones.},
  url       = {http://aclweb.org/anthology/W16-3925}
}