@InProceedings{chang:2016:WNUT,
  author    = {Chang, Ming-Wei},
  title     = {From Entity Linking to Question Answering -- Recent Progress on Semantic Grounding Tasks},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2},
  abstract  = {Entity linking and semantic parsing have been shown to be crucial to important
	applications such as question answering and document understanding. These tasks
	often require structured learning models, which make predictions on multiple
	interdependent variables. In this talk, I argue that carefully designed
	structured learning algorithms play a central role in entity linking and
	semantic parsing tasks. In particular, I will present several new structured
	learning models for entity linking, which jointly detect mentions and
	disambiguate entities as well as capture non-textual information. I will then
	show how to use a staged search procedure to building a state-of-the-art
	knowledge base question answering system. Finally, if time permits, I will
	discuss different supervision protocols for training semantic parsers and the
	value of labeling semantic parses.},
  url       = {http://aclweb.org/anthology/W16-3902}
}