DETECTION OF AGREEMENT vs. DISAGREEMENT IN MEETINGS:TRAINING WITH UNLABELED DATA Export

@proceedings{citeulike:3720733, 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\%.}, author = {Dustin}, citeulike-article-id = {3720733}, citeulike-linkout-0 = {http://www.icsi.berkeley.edu/ftp/pub/speech/papers/hlt03-agree-disagree.pdf}, keywords = {sentiment}, posted-at = {2008-11-28 11:32:02}, priority = {2}, title = {DETECTION OF AGREEMENT vs. DISAGREEMENT IN MEETINGS:TRAINING WITH UNLABELED DATA}, url = {http://www.icsi.berkeley.edu/ftp/pub/speech/papers/hlt03-agree-disagree.pdf} }

TY - CONF ID - citeulike:3720733 L3 - citeulike-article-id:3720733 N2 - 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%. TI - DETECTION OF AGREEMENT vs. DISAGREEMENT IN MEETINGS:TRAINING WITH UNLABELED DATA KW - sentiment AU - Dustin UR - http://www.icsi.berkeley.edu/ftp/pub/speech/papers/hlt03-agree-disagree.pdf ER -