@InProceedings{andryushechkin-wood-oneill:2017:WASSA2017,
  author    = {Andryushechkin, Vladimir  and  Wood, Ian  and  O' Neill, James},
  title     = {NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {175--179},
  abstract  = {This paper describes the entry NUIG in the WASSA 2017 (8th Workshop on
	Computational Approaches to Subjectivity, Sentiment \& Social Media Analysis)
	shared task on emotion recognition.
	The NUIG system used an SVR (SVM regression) and BLSTM ensemble, utilizing
	primarily n-grams (for SVR features) and tweet word embeddings (for BLSTM
	features). Experiments were carried out on several other candidate features,
	some of which were added to the SVR model. 
	Parameter selection for the SVR model was run as a grid search whilst
	parameters for the BLSTM model were selected through a non-exhaustive ad-hoc
	search.},
  url       = {http://www.aclweb.org/anthology/W17-5223}
}