Kernel methods for word sense disambiguation and acronym expansion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The scarcity of manually labeled data for supervised machine learning methods presents a significant limitation on their ability to acquire knowledge. The use of kernels in Support Vector Machines (SVMs) provides an excellent mechanism to introduce prior knowledge into the SVM learners, such as by using unlabeled text or existing ontologies as additional knowledge sources. Our aim is to develop three kernels - one that makes use of knowledge derived from unlabeled text, the second using semantic knowledge from ontologies, and finally a third, additive kernel consisting of the first two kernels - and study their effect on the tasks of word sense disambiguation and automatic expansion of ambiguous acronyms.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Pages1879-1880
Number of pages2
StatePublished - Nov 13 2006
Event21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06 - Boston, MA, United States
Duration: Jul 16 2006Jul 20 2006

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
CountryUnited States
CityBoston, MA
Period7/16/067/20/06

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