@InProceedings{baker-kiela-korhonen:2016:COLING,
  author    = {Baker, Simon  and  Kiela, Douwe  and  Korhonen, Anna},
  title     = {Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2333--2343},
  abstract  = {The conventional solution for handling sparsely labelled data is extensive
	feature engineering. This is time consuming and task and domain specific. We
	present a novel approach for learning embedded features that aims to alleviate
	this problem. Our approach jointly learns embeddings at different levels of
	granularity (word, sentence and document) along with the class labels. The
	intuition is that topic semantics represented by embeddings at multiple levels
	results in better classification. We evaluate this approach in unsupervised and
	semi-supervised settings on two sparsely labelled classification tasks,
	outperforming the handcrafted models and several embedding baselines.},
  url       = {http://aclweb.org/anthology/C16-1220}
}