@InProceedings{jauhar-hovy:2017:starSEM,
  author    = {Jauhar, Sujay Kumar  and  Hovy, Eduard},
  title     = {Embedded Semantic Lexicon Induction with Joint Global and Local Optimization},
  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     = {209--219},
  abstract  = {Creating annotated frame lexicons such as PropBank and FrameNet is expensive
	and labor intensive. We present a method to induce an embedded frame lexicon in
	an minimally supervised fashion using nothing more than unlabeled
	predicate-argument word pairs. We hypothesize that aggregating such pair
	selectional preferences across training leads us to a global understanding that
	captures predicate-argument frame structure. Our approach revolves around a
	novel integration between a predictive embedding model and an Indian Buffet
	Process posterior regularizer. We show, through our experimental evaluation,
	that we outperform baselines on two tasks and can learn an embedded frame
	lexicon that is able to capture some interesting generalities in relation to
	hand-crafted semantic frames.},
  url       = {http://www.aclweb.org/anthology/S17-1025}
}