Combining Multiple Knowledge Sources for Discourse Segmentation
<|reference_start|>Combining Multiple Knowledge Sources for Discourse Segmentation: We 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).<|reference_end|>
@article{litman1995combining,
    title={Combining Multiple Knowledge Sources for Discourse Segmentation},
    author={Diane J. Litman (AT&T Bell Laboratories) and Rebecca J. Passonneau
  (Bellcore)},
    journal={arXiv preprint arXiv:cmp-lg/9505025},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9505025},
    primaryClass={cmp-lg cs.CL}
}