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Sparse bayesian classification of predicate arguments

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Author

Summary, in English

We present an application of Sparse Bayesian Learning to the task of semantic role labeling, and we demonstrate that this method produces smaller classifiers than the popular Support Vector approach. We describe the classification strategy and the features used by the classifier. In particular, the contribution of six parse tree path features is investigated.

Publishing year

2005

Language

English

Pages

177-200

Publication/Series

CoNLL-2005: Proceedings of the Ninth Conference on Computational Natural Language Learning, 43rd Annual Meeting of the Association of Computational Linguistics

Document type

Conference paper

Publisher

Association for Computational Linguistics

Topic

  • Computer Science

Keywords

  • machine learning
  • Natural language processing
  • semantic analysis

Conference name

Ninth Conference on Computational Natural Language Learning

Conference date

2005-06-29 - 2005-06-30

Conference place

Ann Arbor, United States

Status

Published