Paper: A Kernel PCA Method For Superior Word Sense Disambiguation

ACL ID P04-1081
Title A Kernel PCA Method For Superior Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Prin- cipal Component Analysis (KPCA) technique to achieve accuracy superior to the best published indi- vidual models. We present empirical results demon- strating significantly better accuracy compared to the state-of-the-art achieved by either na¨ıve Bayes or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. It is hoped that these highly encouraging first results on KPCA for natural lan- guage processing tasks will inspire further develop- ment of these directions.