@InProceedings{nietopina-johansson:2017:I17-1,
  author    = {Nieto Pi\~{n}a, Luis  and  Johansson, Richard},
  title     = {Training Word Sense Embeddings With Lexicon-based Regularization},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {284--294},
  abstract  = {We propose to improve word sense embeddings by enriching an automatic
	corpus-based method with lexicographic data. Information from a lexicon is
	introduced into the learning algorithm’s objective function through a
	regularizer. The incorporation of lexicographic data yields embeddings that are
	able to reflect expert-defined word senses, while retaining the robustness,
	high quality, and coverage of automatic corpus-based methods. These properties
	are observed in a manual inspection of the semantic clusters that different
	degrees of regularizer strength create in the vector space. Moreover, we
	evaluate the sense embeddings in two downstream applications: word sense
	disambiguation and semantic frame prediction, where they outperform simpler
	approaches. Our results show that a corpus-based model balanced with
	lexicographic data learns better representations and improve their performance
	in downstream tasks.},
  url       = {http://www.aclweb.org/anthology/I17-1029}
}