# Word Sense Disambiguation using Diffusion Kernel PCA

## TL;DR

This paper introduces Diffusion Kernel PCA, a supervised method for word sense disambiguation that leverages semantic diffusion kernels to improve accuracy, especially with limited labeled data.

## Contribution

The paper presents a novel supervised WSD algorithm combining Kernel PCA and semantic diffusion kernels, demonstrating improved performance with scarce labeled data.

## Key findings

- DKPCA outperforms SVM and KPCA on SensEval data.
- Effective with limited labeled data.
- Encouraging results for semi-supervised WSD development.

## Abstract

One of the major problems in natural language processing (NLP) is the word sense disambiguation (WSD) problem. It is the task of computationally identifying the right sense of a polysemous word based on its context. Resolving the WSD problem boosts the accuracy of many NLP focused algorithms such as text classification and machine translation. In this paper, we introduce a new supervised algorithm for WSD, that is based on Kernel PCA and Semantic Diffusion Kernel, which is called Diffusion Kernel PCA (DKPCA). DKPCA grasps the semantic similarities within terms, and it is based on PCA. These properties enable us to perform feature extraction and dimension reduction guided by semantic similarities and within the algorithm. Our empirical results on SensEval data demonstrate that DKPCA achieves higher or very close accuracy results compared to SVM and KPCA with various well-known kernels when the labeled data ratio is meager. Considering the scarcity of labeled data, whereas large quantities of unlabeled textual data are easily accessible, these are highly encouraging first results to develop DKPCA further.