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 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...

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Bibliographic Details
Published inarXiv.org
Main Authors Sipal, Bilge, Ozcan Sari, Teke, Asena, Demirci, Nurullah
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.07.2019
Subjects
Algorithms
Analogies
Diffusion
Feature extraction
Kernels
Machine translation
Natural language processing
Semantics
Word sense disambiguation
Words (language)
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Summary: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.
ISSN:2331-8422