There is an 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.
Pradhan, S., Hacioglu, K., Ward, W., Martin, J. H., & Jurafsky, D. (2003). Semantic Role Parsing: Adding Semantic Structure to Unstructured Text. Proceedings of the 3rd IEEE International Conference on Data Mining (2003, Melbourne, FL), pp. 629-632.
The definitive version is available at https://doi.org/10.1109/ICDM.2003.1250994
3rd IEEE International Conference on Data Mining, ICDM 2003 (2003: Nov. 19-22, Melbourne, FL)
International Standard Book Number (ISBN)
Article - Conference proceedings
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