A problem for RST: the need for multi-level discourse analysis
Computational Linguistics
Choosing a Set of Coherence Relations for Text Generation: A Data-Driven Approach
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
Computing representations of the structure of written discourse
Computing representations of the structure of written discourse
The rhetorical parsing of unrestricted texts: a surface-based approach
Computational Linguistics
A decision-based approach to rhetorical parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
Automatic decision detection in meeting speech
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
A rhetorical syntax-driven model for speech summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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This paper investigates the usefulness of prosodic features in classifying rhetorical relations between utterances in meeting recordings. Five rhetorical relations of contrast, elaboration, summary, question and cause are explored. Three training methods - supervised, unsupervised, and combined - are compared, and classification is carried out using support vector machines. The results of this pilot study are encouraging but mixed, with pairwise classification achieving an average of 68% accuracy in discerning between relation pairs using only prosodic features, but multi-class classification performing only slightly better than chance.