@InProceedings{lakomkin-bothe-wermter:2017:WASSA2017,
  author    = {Lakomkin, Egor  and  Bothe, Chandrakant  and  Wermter, Stefan},
  title     = {GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection},
  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     = {169--174},
  abstract  = {The WASSA 2017 EmoInt shared task has
	the goal to predict emotion intensity values
	of tweet messages. Given the text of
	a tweet and its emotion category (anger,
	joy, fear, and sadness), the participants
	were asked to build a system that assigns
	emotion intensity values. Emotion intensity
	estimation is a challenging problem
	given the short length of the tweets, the
	noisy structure of the text and the lack
	of annotated data. To solve this problem,
	we developed an ensemble of two neural
	models, processing input on the character.
	and word-level with a lexicon-driven
	system. The correlation scores across all
	four emotions are averaged to determine
	the bottom-line competition metric, and
	our system ranks place forth in full intensity
	range and third in 0.5-1 range of intensity
	among 23 systems at the time of
	writing (June 2017).},
  url       = {http://www.aclweb.org/anthology/W17-5222}
}