@InProceedings{wawer-mykowiecka:2017:SENSE2017,
  author    = {Wawer, Aleksander  and  Mykowiecka, Agnieszka},
  title     = {Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms},
  booktitle = {Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {120--125},
  abstract  = {This paper compares two approaches to word sense disambiguation using word
	embeddings trained on unambiguous synonyms. The first is unsupervised method
	based on computing log probability from sequences of word embedding vectors,
	taking into account ambiguous word senses and guessing correct sense from
	context. The second method is supervised. We use a multilayer neural network
	model to learn a context-sensitive transformation that maps an input vector of
	ambiguous word into an output vector representing its sense. We evaluate both
	methods on corpora with manual annotations of word senses from the Polish
	wordnet (plWordnet).},
  url       = {http://www.aclweb.org/anthology/W17-1915}
}