@InProceedings{nguyen-EtAl:2017:starSEM,
  author    = {Nguyen, Dai Quoc  and  Nguyen, Dat Quoc  and  Modi, Ashutosh  and  Thater, Stefan  and  Pinkal, Manfred},
  title     = {A Mixture Model for Learning Multi-Sense Word Embeddings},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {121--127},
  abstract  = {Word embeddings are now a standard technique for inducing meaning
	representations for words. For getting good representations, it is important to
	take into account different senses of a word. In this paper, we propose a
	mixture model for learning multi-sense word embeddings. Our model generalizes
	the previous works in that it allows to induce different weights of different
	senses of a word. The experimental results show that our model outperforms
	previous models on standard evaluation tasks.},
  url       = {http://www.aclweb.org/anthology/S17-1015}
}