@InProceedings{augenstein-sogaard:2017:Short,
  author    = {Augenstein, Isabelle  and  S{\o}gaard, Anders},
  title     = {Multi-Task Learning of Keyphrase Boundary Classification},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {341--346},
  abstract  = {Keyphrase boundary classification (KBC) is the task of detecting keyphrases in
	scientific articles and labelling them with respect to predefined types.
	Although important in practice, this task is so far underexplored, partly due
	to the lack of labelled data. 
	To overcome this, we explore several auxiliary tasks, including semantic
	super-sense tagging and identification of multi-word expressions, and cast the
	task as a multi-task learning problem with deep recurrent neural networks. Our
	multi-task models perform significantly better than previous state of the art
	approaches on two scientific KBC datasets, particularly for long keyphrases.},
  url       = {http://aclweb.org/anthology/P17-2054}
}