Shallow semantic parsing using support vector machines. Pradhan, S., Ward, W., Hacioglu, K., Martin, J., H., & Jurafsky, D. Baseline, Association for Computational Linguistics, 2004.
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Paper Website abstract bibtex
Paper Website abstract bibtex In this paper, we propose a machine learning al- gorithm for shallow semantic parsing, extend- ing the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our al- gorithm is based on Support Vector Machines which we show give an improvement in perfor- mance over earlier classifiers. We show perfor- mance improvements through a number of new features and measure their ability to general- ize to a new test set drawn from the AQUAINT corpus.
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abstract = {In this paper, we propose a machine learning al- gorithm for shallow semantic parsing, extend- ing the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our al- gorithm is based on Support Vector Machines which we show give an improvement in perfor- mance over earlier classifiers. We show perfor- mance improvements through a number of new features and measure their ability to general- ize to a new test set drawn from the AQUAINT corpus.},
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author = {Pradhan, Sameer and Ward, Wayne and Hacioglu, Kadri and Martin, James H and Jurafsky, Dan},
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