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NAACL
2003

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

8 years 11 months ago
Detection Of Agreement vs. Disagreement In Meetings: Training With Unlabeled Data
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%.
Dustin Hillard, Mari Ostendorf, Elizabeth Shriberg
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Dustin Hillard, Mari Ostendorf, Elizabeth Shriberg
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