@InProceedings{li-EtAl:2017:EMNLP20176,
  author    = {Li, Bofang  and  Liu, Tao  and  Zhao, Zhe  and  Tang, Buzhou  and  Drozd, Aleksandr  and  Rogers, Anna  and  Du, Xiaoyong},
  title     = {Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings},
  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     = {2421--2431},
  abstract  = {The number of word embedding models is growing every year. Most of them are
	based on the co-occurrence information of words and their contexts. However, it
	is still an open question what is the best definition of context. We provide a
	systematical investigation of 4 different syntactic context types and context
	representations for learning word embeddings. Comprehensive experiments are
	conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks.
	We hope that this paper, along with the published code, would be helpful for
	choosing the best context type and representation for a given task.},
  url       = {https://www.aclweb.org/anthology/D17-1257}
}