Mixing Weak Learners in Semantic Parsing

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This paper shows results from the application of a novel variant of Random Forests to the shallow semantic parsing problem.

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8 p.

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Nielsen, Rodney D. & Pradhan, Sameer July 2004.

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Description

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.

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  • 2004 Conference on Empirical Methods in Natural Language Processing. July 25-26, 204. Barcelona, Spain.

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  • Publication Title: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing
  • Peer Reviewed: Yes

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UNT Scholarly Works

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  • July 2004

Added to The UNT Digital Library

  • Nov. 30, 2017, 9:17 a.m.

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Nielsen, Rodney D. & Pradhan, Sameer. Mixing Weak Learners in Semantic Parsing, paper, July 2004; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc1042593/: accessed December 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.