@InProceedings{denis-ralaivola:2017:EACLlong,
  author    = {Denis, Pascal  and  Ralaivola, Liva},
  title     = {Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {775--784},
  abstract  = {This paper presents a new, efficient method for learning task-specific
	word vectors using a variant of the Passive-Aggressive
	algorithm. Specifically, this algorithm learns a word embedding matrix
	in tandem with the classifier parameters in an online fashion, solving
	a bi-convex constrained optimization at each iteration. We provide a
	theoretical analysis of this new algorithm in terms of regret bounds,
	and evaluate it on both synthetic data and NLP classification
	problems, including text classification and sentiment analysis. In the
	latter case, we compare various pre-trained word vectors to initialize
	our word embedding matrix, and show that the matrix learned by our
	algorithm vastly outperforms the initial matrix, with performance
	results comparable or above the state-of-the-art on these tasks.},
  url       = {http://www.aclweb.org/anthology/E17-1073}
}