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We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speechact-like units such as STATEMENT,QUESTION, BACKCHANNEL,AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence.(More)
We describe a statistical approach for modeling dialog acts in conversational speech, i.e., speechact-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialog acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialog act sequence. The(More)
Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be(More)
From spring 1990 through fall 1991, we performed a battery of small experiments to test the effectiveness of supplementing knowledge-based techniques with probabilistic models. This paper reports our experiments in predicting parts of speech of highly ambiguous words, predicting the intended interpretation of an utterance when more than one interpretation(More)
We describe a new approach for statistical modeling and detection of discourse structure for natural conversational speech. Our model is based on 42 ‘Dialog Acts’ (DAs), (question, answer, backchannel, agreement, disagreement, apology, etc). We labeled 1155 conversations from the Switchboard (SWBD) database (Godfrey et al. 1992) of human-to-human telephone(More)
We report here on our experiments with POST (Part of Speech Tagger) to address problems of ambiguity and of understanding unknown words. Part of speech tagging, per se, is a well understood problem. Our paper reports experiments in three important areas: handling unknown words, l imit ing the size of the training set, and returning a set of the most likely(More)