Kernel methods for word sense disambiguation and acronym expansion

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.