@InProceedings{yuan-EtAl:2016:COLING,
  author    = {Yuan, Dayu  and  Richardson, Julian  and  Doherty, Ryan  and  Evans, Colin  and  Altendorf, Eric},
  title     = {Semi-supervised Word Sense Disambiguation with Neural Models},
  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     = {1374--1385},
  abstract  = {Determining the intended sense of words in text -- word sense disambiguation
	(WSD) -- is a long-standing problem in natural language processing. Recently,
	researchers have shown promising results using word vectors extracted from a
	neural network language model as features in WSD algorithms. However, a simple
	average or concatenation of word vectors for each word in a text loses the
	sequential and syntactic information of the text. 
	In this paper, we study WSD with a sequence learning neural net, LSTM, to
	better capture the sequential and syntactic patterns of the text. To alleviate
	the lack of training data in all-words WSD, we employ the same LSTM in a
	semi-supervised label propagation classifier. We demonstrate state-of-the-art
	results, especially on verbs.},
  url       = {http://aclweb.org/anthology/C16-1130}
}