The UNT College of Engineering strives to educate and train engineers and technologists who have the vision to recognize and solve the problems of society. The college comprises six degree-granting departments of instruction and research.
This paper shows results from the application of a novel variant of Random Forests to the shallow semantic parsing problem.
Physical Description
8 p.
Notes
Abstract: We apply a novel variant of Random Forests (Breiman, 2001) to the shallow semantic parsing problem and show extremely promising results. The final system has a semantic role classification accuracy of 88.3% using PropBank gold-standard parses. These results are better than all others published except those of the Support Vector Machine (SVM) approach implemented by Pradhan et al. (2003) and Random Forests have numerous advantages over SVMs including simplicity, faster training and classification, easier multi-class classification, and easier problem-specific customization. We also present new features which result in a 1.1% gain in classification accuracy and describe a technique that results in a 97% reduction in the feature space with no significant degradation in accuracy.
Publication Title:
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing
Peer Reviewed:
Yes
Collections
This paper is part of the following collection of related materials.
UNT Scholarly Works
Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.
