@inproceedings{1219040,
	title = {Identifying agreement and disagreement in conversational speech: use of Bayesian networks to model pragmatic dependencies},
	address = {Morristown, NJ, USA},
	author = {Michel Galley and Kathleen McKeown and Julia Hirschberg and Elizabeth Shriberg},
	booktitle = {ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics},
	pages = {669},
	publisher = {Association for Computational Linguistics},
	url = {http://portal.acm.org/citation.cfm?id=1219040},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/2bba03b0bba96fcfa58abceb140cae4d3/wnpxrz},
	description = {Identifying agreement and disagreement in conversational speech},
	abstract = {We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. Our approach achieves 86.9% accuracy, a 4.9% increase over previous work.},
	doi = {http://dx.doi.org/10.3115/1218955.1219040}, location = {Barcelona, Spain},
	keywords = {agreement bayesian imported speech }
}