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Detection of agreement vs. disagreement in meetings: training with unlabeled data
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Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2 table of contents
Edmonton, Canada
Pages: 34 - 36  
Year of Publication: 2003
Authors
Dustin Hillard  University of Washington, EE
Mari Ostendorf  University of Washington, EE
Elizabeth Shriberg  SRI International and ICSI
Publisher
Association for Computational Linguistics  Morristown, NJ, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 13,   Citation Count: 7
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DOI Bookmark: 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%.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
D. Baron et al. 2002. Automatic punctuation and disfluency detection in multi-party meetings using prosodic and lexical cues. In Proc. ICSLP, pages 949--952.
 
2
S. Bhagat, H. Carvey, and E. Shriberg. 2003. Automatically generated prosodic cues to lexically ambiguous dialog acts in multi-party meetings. In ICPhS.
 
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J. Chu-Carroll. 1998. A statistical model for discourse act recognition in dialogue interactions. In Applying Machine Learning to Discourse Processing. Papers from the 1998 AAAI Spring Symposium, pages 12--17.
 
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7
E. Shriberg et al. 1998. Can prosody aid the automatic classification of dialog acts in conversational speech? Language and Speech, 41(3--4), pages 439--487.
 
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E. Shriberg et al. 2001. Observations on overlap: Findings and implications for automatic processing of multi-party conversation. In Proc. Eurospeech, pages 1359--1362.
 
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CITED BY  7
 
 
 
 
Collaborative Colleagues:
Dustin Hillard: colleagues
Mari Ostendorf: colleagues
Elizabeth Shriberg: colleagues