Semantic Role Chunking Combining Complementary Syntactic Views. Pradhan, S., Hacioglu, K., Ward, W., Martin, J., H., & Jurafsky, D. In Proceedings of the Ninth Conference on Computational Natural Language Learning CoNLL2005, pages 217-220, 2005. Association for Computational Linguistics. ![Semantic Role Chunking Combining Complementary Syntactic Views [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunking paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse using Support Vector Machine classifiers. The resulting semantic role labels are converted to an IOB representation. These IOB representations are used as additional features, along with flat syntactic chunks, by a chunking SVM classifier that produces the final SRL output. This strategy for combining features from three different syntactic views gives a significant improvement in performance over roles produced by using any one of the syntactic views individually.
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abstract = {This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunking paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse using Support Vector Machine classifiers. The resulting semantic role labels are converted to an IOB representation. These IOB representations are used as additional features, along with flat syntactic chunks, by a chunking SVM classifier that produces the final SRL output. This strategy for combining features from three different syntactic views gives a significant improvement in performance over roles produced by using any one of the syntactic views individually.},
bibtype = {inProceedings},
author = {Pradhan, Sameer and Hacioglu, Kadri and Ward, Wayne and Martin, James H and Jurafsky, Daniel},
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