@InProceedings{bhatia-lau-baldwin:2016:COLING,
  author    = {Bhatia, Shraey  and  Lau, Jey Han  and  Baldwin, Timothy},
  title     = {Automatic Labelling of Topics with Neural 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     = {953--963},
  abstract  = {Topics generated by topic models are typically represented as list of
	terms. To reduce the cognitive overhead of interpreting these topics for
	end-users, we propose labelling a topic with a succinct phrase that
	summarises its theme or idea. Using Wikipedia document titles as label
	candidates, we compute neural embeddings for documents and words to
	select the most relevant labels for topics. Comparing to a
	state-of-the-art topic labelling system, our methodology is simpler,
	more efficient and finds better topic labels.},
  url       = {http://aclweb.org/anthology/C16-1091}
}