ML Anthology

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

Bram Vanschoenwinkel, Bernard Manderick
IJCAI 2003 pp. 133-140
/ijcai/2003/vanschoenwinkel2003ijcai-weighted/

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

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Text

Vanschoenwinkel and Manderick. "A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines." International Joint Conference on Artificial Intelligence, 2003.

Markdown

[Vanschoenwinkel and Manderick. "A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/vanschoenwinkel2003ijcai-weighted/)

BibTeX

@inproceedings{vanschoenwinkel2003ijcai-weighted,
  title     = {{A Weighted Polynomial Information Gain Kernel for Resolving Prepositional Phrase Attachment Ambiguities with Support Vector Machines}},
  author    = {Vanschoenwinkel, Bram and Manderick, Bernard},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2003},
  pages     = {133-140},
  url       = {https://mlanthology.org/ijcai/2003/vanschoenwinkel2003ijcai-weighted/}
}