Shallow Semantic Parsing using Support Vector Machines

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.

• Type: Conference
• Year: 2004
• Where: NAACL
• Authors: Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James H. Martin, Daniel Jurafsky
• Keywords: Machine Learning Algorithm, NAACL 2004, NAACL 2007, Shallow Semantic Parsing, Support Vector Machines