@InProceedings{jansson-liu:2017:WNUT,
  author    = {Jansson, Patrick  and  Liu, Shuhua},
  title     = {Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {154--159},
  abstract  = {This paper reports our participation in the W-NUT 2017 shared task on emerging
	and rare entity recognition from user generated noisy text such as tweets,
	online reviews and forum discussions. To accomplish this challenging task, we
	explore an approach that combines LDA topic modelling with deep learning on
	word level and character level embeddings. The LDA topic modelling generates
	topic representation for each tweet which is used as a feature for each word in
	the tweet. The deep learning component consists of two-layer bidirectional LSTM
	and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity
	and 37.77 on surface forms. Our new experiments after submission reached a best
	performance of 41.81 on entity and 40.57 on surface forms.},
  url       = {http://www.aclweb.org/anthology/W17-4420}
}