@InProceedings{beck:2017:I17-2,
  author    = {Beck, Daniel},
  title     = {Modelling Representation Noise in Emotion Analysis using Gaussian Processes},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {140--145},
  abstract  = {Emotion Analysis is the task of modelling latent emotions present in natural
	language. Labelled datasets for this task are scarce so learning good input
	text representations is not trivial. Using averaged word embeddings is a simple
	way to leverage unlabelled corpora to build text representations but this
	approach can be prone to noise either coming from the embedding themselves or
	the averaging procedure. In this paper we propose a model for Emotion Analysis
	using Gaussian Processes and kernels that are better suitable for functions
	that exhibit noisy behaviour. Empirical evaluations in a emotion prediction
	task show that our model outperforms commonly used baselines for regression.},
  url       = {http://www.aclweb.org/anthology/I17-2024}
}