Word Sense Disambiguation using Diffusion Kernel PCA

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is t he task of computationally identifying the right sense of a polysemou s word based on its context. Resolving the WSD problem boosts the accurac y of many NLP focused algorithms such as text classification and machi ne translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, whi ch is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semant ic similarities within terms, and it is based on PCA. These prop erties enable us to perform feature extraction and dimension reducti on guided by semantic similarities and within the algorithm. Our empiri cal results on SensEval data demonstrate that DKPCA achieves higher or ver y close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering t he scarcity of labeled data, whereas large quantities of unlabeled text ual data are easily accessible, these are highly encouraging first resul ts to develop DKPCA further.