@InProceedings{maredia-EtAl:2017:starSEM,
  author    = {Maredia, Angel  and  Schechtman, Kara  and  Levitan, Sarah Ita  and  Hirschberg, Julia},
  title     = {Comparing Approaches for Automatic Question Identification},
  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     = {110--114},
  abstract  = {Collecting spontaneous speech corpora that are open-ended, yet topically
	constrained, is increasingly popular for research in spoken dialogue systems
	and speaker state, inter alia. Typically, these corpora are labeled by human
	annotators, either in the lab or through crowd-sourcing; however, this is
	cumbersome and time-consuming for large corpora. We present four different
	approaches to automatically tagging a corpus when general topics of the
	conversations are known. We develop these approaches on the Columbia X-Cultural
	Deception corpus and find accuracy that significantly exceeds the baseline.
	Finally, we conduct a cross-corpus evaluation by testing the best performing
	approach on the Columbia/SRI/Colorado corpus.},
  url       = {http://www.aclweb.org/anthology/S17-1013}
}