@InProceedings{kang-EtAl:2016:NLPTEA2016,
  author    = {Kang, Hong Jin  and  Chen, Tao  and  Chandrasekaran, Muthu Kumar  and  Kan, Min-Yen},
  title     = {A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {30--39},
  abstract  = {Word embeddings are now ubiquitous forms of word representation in
	natural language processing.  There have been applications of 
	word embeddings for monolingual word sense disambiguation (WSD) in English,
	but few comparisons have been done.  This paper attempts to bridge
	that gap by examining popular embeddings for the task of monolingual
	English WSD.  Our simplified method leads to comparable
	state-of-the-art performance without expensive retraining.
	Cross-Lingual WSD -- where the word senses of a word in a source
	language come from a separate target translation language --
	can also assist in language learning; for example, when providing
	translations of target vocabulary for learners.  Thus we have also
	applied word embeddings to the novel task of cross-lingual WSD for
	Chinese and provide a public dataset for further benchmarking.
	We have also experimented with using word embeddings for LSTM networks
	and found surprisingly that a basic LSTM network does not work well.
	We discuss the ramifications of this outcome.},
  url       = {http://aclweb.org/anthology/W16-4905}
}