Shallow Semantic Parsing using Support Vector Machines

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In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.

Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James

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Machine Learning Algorithm | NAACL 2004 | NAACL 2007 | Shallow Semantic Parsing | Support Vector Machines |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2004
Where	NAACL
Authors	Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James H. Martin, Daniel Jurafsky

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Shallow Semantic Parsing using Support Vector Machines

Machine Learning Algorithm | NAACL 2004 | NAACL 2007 | Shallow Semantic Parsing | Support Vector Machines |

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