Combining Multiple Knowledge Sources for Discourse Segmentation

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dc.creatorLitman, Diane J.
dc.creatorPassonneau, Rebecca J.
dc.date1995-05-10
dc.date.accessioned2026-07-07T09:09:51Z
dc.date.available2026-07-07T09:09:51Z
dc.descriptionWe predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing segmentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an independent test set (both methods), and using cross-validation (machine learning).
dc.description8 pages. Self-contained latex source. To appear in Proceedings of the 33rd ACL, 1995. (This replacement version revised so that no lines exceed 80 characters.)
dc.identifierhttps://arxiv.org/abs/cmp-lg/9505025
dc.identifierhttp://arxiv.org/abs/cmp-lg/9505025
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151184
dc.subjectComputation and Language
dc.titleCombining Multiple Knowledge Sources for Discourse Segmentation
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