@InProceedings{sanu-EtAl:2017:EMNLP2017,
  author    = {Sanu, Joseph  and  Xu, Mingbin  and  Jiang, Hui  and  Liu, Quan},
  title     = {Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {310--315},
  abstract  = {In this paper, we propose to learn word embeddings based on the recent
	fixed-size ordinally forgetting encoding (FOFE) method, which can almost
	uniquely encode any variable-length sequence into a fixed-size representation.
	We use FOFE to fully encode the left and right context of each word in a corpus
	to construct a novel word-context matrix, which is further weighted and
	factorized using truncated SVD to generate low-dimension word embedding
	vectors. We evaluate this alternate method in encoding word-context statistics
	and show the new FOFE method has a notable effect on the resulting word
	embeddings. Experimental results on several popular word similarity tasks have
	demonstrated that the proposed method  outperforms other SVD models that use
	canonical count based techniques to generate word context matrices.},
  url       = {https://www.aclweb.org/anthology/D17-1031}
}