Paper: Detection Of Agreement Vs. Disagreement In Meetings: Training With Unlabeled Data

ACL ID N03-2012
Title Detection Of Agreement Vs. Disagreement In Meetings: Training With Unlabeled Data
Venue Human Language Technologies
Session Short Paper
Year 2003
Authors

To support summarization of automatically transcribed meetings, we introduce a classifier to recognize agreement or disagreement utter- ances, 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%.