2004 ConvolutionKernelsForSRL

(Moschitti, 2004) ⇒ Alessandro Moschitti. (2004). “A study on Convolution Kernels for Shallow Semantic Parsing.” In: Proceedings of the 42-nd Conference on Association for Computational Linguistic (ACL 2004).

Subject Headings: Convolution Kernel Function, Semantic Role Labeling Task

Notes

Website: http://ai-nlp.info.uniroma2.it/moschitti/TK1.2-software/Tree-Kernel.htm

Cited By

Quotes

Abstract

In this paper we have designed and experimented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process structured representations. Support Vector Machines (SVMs), using a combination of such kernels and the at feature kernel, classify PropBank predicate arguments with accuracy higher than the current argument classification state of-the-art. Additionally, experiments on FrameNet data have shown that SVMs are appealing for the classification of semantic roles even if the proposed kernels do not produce any improvement.

References

Michael Collins and Nigel Du y. (2002). New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In proceeding of ACL-02.
Michael Collins. (1997). Three generative, lexicalized models for statistical parsing. In: Proceedings of the ACL-97, pages 16{23, Somerset, New Jersey.
Nello Cristianini and John Shawe-Taylor. (2000). An introduction to Support Vector Machines. Cambridge University Press.
Charles J. Fillmore. (1982). Frame semantics. In Linguistics in the Morning Calm, pages 111{137.
Daniel Gildea and Daniel Jurasfky. (2002). Automatic labeling of semantic roles. Computational Linguistic.
Daniel Gildea and Martha Palmer. (2002). The necessity of parsing for predicate argument recognition. In: Proceedings of ACL-02, Philadelphia, PA.
R. Jackendo. (1990). Semantic Structures, Current Studies in Linguistics series. Cambridge, Massachusetts: The MIT Press.
Thorsten Joachims. (1999). Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning.
Paul Kingsbury and Martha Palmer. (2002). From treebank to propbank. In: Proceedings of LREC-02, Las Palmas, Spain.
M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. (1993). Building a large annotated corpus of english: The penn treebank. Computational Linguistics.
Alessandro Moschitti and Cosmin Adrian Bejan. (2004). A semantic kernel for predicate argument classification. In: Proceedings of CoNLL-04, Boston, USA.
Kadri Hacioglu, Sameer Pradhan, Wayne Ward, James H. Martin, and Daniel Jurafsky. (2003). Shallow Semantic Parsing Using Support Vector Machines. TR-CSLR-2003-03, University of Colorado.
Mihai Surdeanu, Sanda M. Harabagiu, John Williams, and John Aarseth. (2003). Using predicate-argument structures for information extraction. In: Proceedings of ACL-03, Sapporo, Japan.
Vladimir N. Vapnik. (1995). The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc.

,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 ConvolutionKernelsForSRL	Alessandro Moschitti			A study on Convolution Kernels for Shallow Semantic Parsing			http://ai-nlp.info.uniroma2.it/moschitti/articles/ACL2004.pdf