@InProceedings{melamud-dagan-goldberger:2017:EMNLP2017,
  author    = {Melamud, Oren  and  Dagan, Ido  and  Goldberger, Jacob},
  title     = {A Simple Language Model based on PMI Matrix Approximations},
  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     = {1860--1865},
  abstract  = {In this study, we introduce a new approach for learning language models by
	training them to estimate word-context pointwise mutual information (PMI), and
	then deriving the desired conditional probabilities from PMI at test time.
	Specifically, we show that with minor modifications to word2vec's algorithm, we
	get principled language models that are closely related to the well-established
	Noise Contrastive Estimation (NCE) based language models. A compelling aspect
	of our approach is that our models are trained with the same simple negative
	sampling objective function that is commonly used in word2vec to learn word
	embeddings.},
  url       = {https://www.aclweb.org/anthology/D17-1198}
}