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. 1

There is a ever-growing need to add structure in the form of semantic markup to the huge amounts of unstructured text data now available. We present the technique of shallow semantic parsing, the process of assigning a simple WHO did WHAT to WHOM, etc., structure to sentences in text, as a useful tool in achieving this goal. We formulate the semantic parsing problem as a classification problem using Support Vector Machines. Using a hand-labeled training set and a set of features drawn from earlier work together with some feature enhancements, we demonstrate a system that performs better than all other published results on shallow semantic parsing.