Manually vs. Automatically Labelled Data in Discourse Relation Classification: Effects of Example and Feature Selection

@article{Sporleder2007ManuallyVA,
  title={Manually vs. Automatically Labelled Data in Discourse Relation Classification: Effects of Example and Feature Selection},
  author={Caroline Sporleder},
  journal={LDV Forum},
  year={2007},
  volume={22},
  pages={1-20}
}
We explore the task of predicting which discourse relation holds between two text spans in which the relation is not signalled by an unambiguous discourse marker. It has been proposed that automatically labelled data, which can be derived from examples in which a discourse relation is unambiguously signalled, could be used to train a machine learner to perform this task reasonably well. However, more recent results suggest that there are problems with this approach, probably due to the fact… CONTINUE READING

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RST Discourse Treebank

  • L. Carlson, D. Marcu, M. E. Okurowski
  • Linguistic Data Consortium.
  • 2002
Highly Influential
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