Paper: Combining Multiple Knowledge Sources For Discourse Segmentation

ACL ID P95-1015
Title Combining Multiple Knowledge Sources For Discourse Segmentation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1995
Authors

We predict discourse segment boundaries from linguistic features of utterances, using a corpus of spoken narratives as data. We present two methods for developing seg- mentation algorithms from training data: hand tuning and machine learning. When multiple types of features are used, results approach human performance on an inde- pendent test set (both methods), and using cross-validation (machine learning).