C4.5: programs for machine learning
C4.5: programs for machine learning
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
Genre distinctions for discourse in the Penn TreeBank
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
The exploitation of spatial information in narrative discourse
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Granularity in natural language discourse
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
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We present the results of several machine learning tasks designed to predict rhetorical relations that hold between clauses in discourse. We demonstrate that organizing rhetorical relations into different granularity categories (based on relative degree of detail) increases average prediction accuracy from 58% to 70%. Accuracy further increases to 80% with the inclusion of clause types. These results, which are competitive with existing systems, hold across several modes of written discourse and suggest that features of information structure are an important consideration in the machine learnability of discourse.