A Kernel PCA Method for Superior Word Sense Disambiguation

We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either na

• Type: Conference
• Year: 2004
• Where: ACL
• Authors: Dekai Wu, Weifeng Su, Marine Carpuat
• Keywords: ACL 2004, ACL 2007, Better Accuracy, Kernel Principal Component Analysis, Maximum Entropy Models