We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Prin- cipal Component Analysis (KPCA) technique to achieve accuracy ...
A new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to ...
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy ...
We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy ...
Abstract. We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Prin- cipal Component Analysis (KPCA) technique to.
This paper proposes a novel methodology for extending syntactic parse trees, based on word sense disambiguation and context specific opinion-related features.
In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Principal Component Analysis (KPCA), with ...
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We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy ...
A Kernel PCA Method for Superior Word Sense Disambiguation. Author. Wu, Dekai and Su, Weifeng and Carpuat, Marine. Conference. Proceedings of the 42nd Annual ...
Apr 9, 2022 · Dekai Wu , Weifeng Su , Marine Carpuat: A Kernel PCA Method for Superior Word Sense Disambiguation. ACL 2004: 637-644. manage site settings.