@InProceedings{amjadian-EtAl:2016:Computerm2016,
  author    = {Amjadian, Ehsan  and  Inkpen, Diana  and  Paribakht, Tahereh  and  Faez, Farahnaz},
  title     = {Local-Global Vectors to Improve Unigram Terminology Extraction},
  booktitle = {Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2--11},
  abstract  = {The present paper explores a novel method that integrates efficient distributed
	representations with terminology extraction. We show that the information from
	a small number of observed instances can be combined with local and global word
	embeddings to remarkably improve the term extraction results on unigram terms.
	To do so we pass the terms extracted by other tools to a filter made of the
	local-global embeddings and a classifier which in turn decides whether or not a
	term candidate is a term. The filter can also be used as a hub to merge
	different term extraction tools into a single higher-performing system. We
	compare filters that use the skip-gram architecture and filters that employ the
	CBOW architecture for the task at hand.
	Author{4}{Affiliation}},
  url       = {http://aclweb.org/anthology/W16-4702}
}