@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},
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=1219040},
        location = {Barcelona, Spain},
	doi = {http://dx.doi.org/10.3115/1218955.1219040},
	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.},
	biburl = {http://www.bibsonomy.org/bibtex/2bba03b0bba96fcfa58abceb140cae4d3/wnpxrz},
	keywords = {bayesian agreement speech imported}
}

@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},
        year = 2003,
        url = {http://portal.acm.org/citation.cfm?id=1073483.1073495},
        location = {Edmonton, Canada},
	doi = {http://dx.doi.org/10.3115/1073483.1073495},
	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%.},
	biburl = {http://www.bibsonomy.org/bibtex/2052086fcc54bcd05d78760afea9d67d0/wnpxrz},
	keywords = {machinelearning imported agreement}
}