Please use this identifier to cite or link to this item:
doi:10.22028/D291-25303
Title: | Learning dialog act processing |
Author(s): | Wermter, Stefan Löchel, Matthias |
Language: | English |
Year of Publication: | 1996 |
SWD key words: | Künstliche Intelligenz |
Free key words: | artificial intelligence |
DDC notations: | 004 Computer science, internet |
Publikation type: | Report |
Abstract: | In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plausibility method, produces a segmentation and dialog act assignment for all utterances in a robust manner, and reduces knowledge engineering since it can be bootstrapped from rather small corpora. Therefore, we consider this new approach as very promising for learning dialog act processing. |
Link to this record: | urn:nbn:de:bsz:291-scidok-53317 hdl:20.500.11880/25359 http://dx.doi.org/10.22028/D291-25303 |
Series name: | Vm-Report / Verbmobil, Verbundvorhaben, [Deutsches Forschungszentrum für Künstliche Intelligenz] |
Series volume: | 139 |
Date of registration: | 12-Jun-2013 |
Faculty: | SE - Sonstige Einrichtungen |
Department: | SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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report_139_96.pdf | 190,36 kB | Adobe PDF | View/Open |
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