@InProceedings{hasan-curry:2017:EMNLP2017,
  author    = {Hasan, Souleiman  and  Curry, Edward},
  title     = {Word Re-Embedding via Manifold Dimensionality Retention},
  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     = {321--326},
  abstract  = {Word embeddings seek to recover a Euclidean metric space by mapping words into
	vectors, starting from words co-occurrences in a corpus. Word embeddings may
	underestimate the similarity between nearby words, and overestimate it between
	distant words in the Euclidean metric space. In this paper, we re-embed
	pre-trained word embeddings with a stage of manifold learning which retains
	dimensionality. We show that this approach is
	theoretically founded in the metric recovery paradigm, and empirically show
	that it can improve on state-of-the-art embeddings in word similarity tasks 0.5
	- 5.0% points depending on the original space.},
  url       = {https://www.aclweb.org/anthology/D17-1033}
}