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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Translation Memory Technology Assessment Van Ess-Dykema, Carol; Converse, Susan; White, John S... In: DTIC (2009) BASE Show details 2 Special Issue on Embedded MT Systems; Overview Voss, Clare R.; Van Ess-Dykema, Carol J. In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 17 (2002) 4, 307-320 OLC Linguistik Show details 3 Telicity as a Cue to Temporal and Discourse Structure in Chinese-English Machine Translation Olsen, Mari; Traum, David; Van Ess-Dykema, Carol... In: DTIC (2000) BASE Show details 4 When is an Embedded MT System 'Good Enough' for Filtering? Voss, Clare R.; Van Ess-Dykema, Carol In: DTIC (2000) BASE Show details 5 Dialogue act modeling for automatic tagging and recognition of conversational speech Stolcke, Andreas; Coccaro, Noah; Bates, Rebecca. - : MIT Press, 2000 BASE Show details 6 Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech? Shriberg, Elizabeth; Bates, Rebecca; Taylor, Paul. - : Kingston Press Services Ltd., 1998 BASE Show details 7 Automatic detection of discourse structure for speech recognition and understanding. Jurafsky, Daniel; Bates, Rebecca; Coccaro, Noah; Martin, Rachel; Meteer, Marie; Ries, Klaus; Shriberg, Elizabeth; Stolcke, Andreas; Taylor, Paul A; Van Ess-Dykema, Carol. - : IEEE, 1997 Abstract: 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 conversations with these 42 types and trained a Dialog Act detector based on three distinct knowledge sources: sequences of words which characterize a dialog act, prosodic features which characterize a dialog act, and a statistical Discourse Grammar. Our combined detector, although still in preliminary stages, already achieves a 65% Dialog Act detection rate based on acoustic waveforms, and 72% accuracy based on word transcripts. Using this detector to switch among the 42 Dialog- Act-Specific trigram LMs also gave us an encouraging but not statistically significant reduction in SWBD word error. URL: http://hdl.handle.net/1842/1232 BASE Hide details 8 Discourse Processing of Dialogues with Multiple Threads ... Rose', Carolyn Penstein; Di Eugenio, Barbara; Levin, Lori S.. - : arXiv, 1995 BASE Show details 9 Discourse Processing of Dialogues with Multiple Threads Penstein Rose, Carolyn; Di Eugenio, Barbara; Levin, Lori S... In: DTIC (1995) BASE Show details

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