@inproceedings{1073495,
	title = {Detection of agreement vs. disagreement in meetings: training with unlabeled data},
	address = {Morristown, NJ, USA},
	author = {Dustin Hillard and Mari Ostendorf and Elizabeth Shriberg},
	booktitle = {NAACL '03: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology},
	pages = {34--36},
	publisher = {Association for Computational Linguistics},
	url = {http://portal.acm.org/citation.cfm?id=1073483.1073495},
	year = {2003},
	biburl = {http://www.bibsonomy.org/bibtex/2052086fcc54bcd05d78760afea9d67d0/wnpxrz},
	description = {Detection of agreement vs. disagreement in meetings},
	abstract = {To support summarization of automatically transcribed meetings, we introduce a classifier to recognize agreement or disagreement utterances, utilizing both word-based and prosodic cues. We show that hand-labeling efforts can be minimized by using unsupervised training on a large unlabeled data set combined with supervised training on a small amount of data. For ASR transcripts with over 45% WER, the system recovers nearly 80% of agree/disagree utterances with a confusion rate of only 3%.},
	doi = {http://dx.doi.org/10.3115/1073483.1073495}, location = {Edmonton, Canada},
	keywords = {agreement imported machinelearning }
}