Paper: Domain Kernels For Word Sense Disambiguation

ACL ID P05-1050
Title Domain Kernels For Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005

In this paper we present a supervised Word Sense Disambiguation methodol- ogy, that exploits kernel methods to model sense distinctions. In particular a combi- nation of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We de ned a ker- nel function, namely the Domain Kernel, that allowed us to plug external knowl- edge into the supervised learning pro- cess. External knowledge is acquired from unlabeled data in a totally unsupervised way, and it is represented by means of Do- main Models. We evaluated our method- ology on several lexical sample tasks in different languages, outperforming sig- ni cantly the state-of-the-art for each of them, while reducing the amount of la- beled training data required for learning.