@InProceedings{cotterell-EtAl:2017:EACLshort,
  author    = {Cotterell, Ryan  and  Poliak, Adam  and  Van Durme, Benjamin  and  Eisner, Jason},
  title     = {Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {175--181},
  abstract  = {The popular skip-gram model induces word embeddings by exploiting the signal
	from word-context coocurrence. We offer a new interpretation of skip-gram based
	on exponential family PCA-a form of matrix factorization to generalize the
	skip-gram model to tensor factorization. In turn, this lets us train embeddings
	through richer higher-order coocurrences, e.g., triples that include positional
	information (to incorporate syntax) or morphological information (to share
	parameters across related words). We experiment on 40 languages and show our
	model improves upon skip-gram.},
  url       = {http://www.aclweb.org/anthology/E17-2028}
}