Proceedings

A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines

Bram Vanschoenwinkel, Bernard Manderick

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Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 133-139.

Abstract

We introduce a new kernel for Support Vector Machine learning in a natural language setting. As a case study to incorporate domain knowledge into a kernel, we consider the problem of resolving Prepositional Phrase attachment ambiguities. The new kernel is derived from a distance function that proved to be succesful in memory-based learning. We start with the Simple Overlap Metric from which we derive a Simple Overlap Kernel and extend it with Information Gain Weighting. Finally, we combine it with a polynomial kernel to increase the dimensionality of the feature space. The closure properties of kernels guarantee that the result is again a kernel. This kernel achieves high classification accuracy and is efficient in both time and space usage. We compare our results with those obtained by memory-based and other learning methods. They make clear that the proposed kernel achieves a higher classification accuracy.

Bram Vanschoenwinkel, and Bernard Manderick. 2003. A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03. IJCAI, 133–139. https://ijcai.org/proceedings/2003/19
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