Augmenting Ensemble Classification for Word Sense Disambiguation with a Kernel PCA Model
Marine Jacinthe Carpuat, Weifeng Su, Dekai Wu · Meeting of the Association for Computational Linguistics · 2004
The HKUST word sense disambiguation systems benefit from a new nonlinear Kernel Principal Component Analysis (KPCA) based disambiguation technique. We discuss and analyze results from the Senseval-3 English, Chinese, and Multilingual Lexical Sample data sets. Among an ensemble of four different kinds of voted models, the KPCA-based model, along with the maximum entropy model, outperforms the boosting model and