---
title: "A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines"
authors:
  - "Bram Vanschoenwinkel"
  - "Bernard Manderick"
doi: ""
url: "https://ijcai.org/proceedings/2003/19"
pdf: "https://ijcai.org/proceedings/2003/0019.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "133-139"
track: ""
keywords:
---

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

Bram Vanschoenwinkel, Bernard Manderick

*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.