Detection of agreement vs. disagreement in meetings: training with unlabeled data
NAACL '03: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, page 34--36. Morristown, NJ, USA, Association for Computational Linguistics, (2003)DOI: http://dx.doi.org/10.3115/1073483.1073495
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%.
Description
Detection of agreement vs. disagreement in meetings
