@InProceedings{nguyen-schulteimwalde-vu:2016:COLING,
  author    = {Nguyen, Kim Anh  and  Schulte im Walde, Sabine  and  Vu, Ngoc Thang},
  title     = {Neural-based Noise Filtering from Word Embeddings},
  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     = {2699--2707},
  abstract  = {Word embeddings have been demonstrated to benefit NLP tasks impressively. Yet,
	there is room for improvements in the vector representations, because current
	word embeddings typically contain unnecessary information, i.e., noise. We
	propose two novel models to improve word embeddings by unsupervised learning,
	in order to yield word denoising embeddings. The word denoising embeddings are
	obtained by strengthening salient information and weakening noise in the
	original word embeddings, based on a deep feed-forward neural network filter.
	Results from benchmark tasks show that the filtered word denoising embeddings
	outperform the original word embeddings.},
  url       = {http://aclweb.org/anthology/C16-1254}
}