Shallow Semantic Parsing using Support Vector Machines. Pradhan, S., Ward, W., & Hacioglu, K. In HLT-NAACL 2004: Main Proceedings, volume 2004, of HLT-NAACL '04, pages 11-39, 2004. Kluwer Academic Publishers. 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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