Word sense disambiguation using semantic kernels with class-based term values

Authors

AYŞE BERNA ALTINEL
MURAT CAN GANİZ
BİLGE ŞİPAL
EREN CAN ERKAYA
ONUR CAN YÜCEDAĞ
MUHAMMED ALİ DOĞAN

DOI

10.3906/elk-1805-131

Abstract

In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?, 6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score.

Keywords

Word sense disambiguation, semantic kernel, classification, term relevance values, sprinkling

First Page

3180

Last Page

3194

Recommended Citation

ALTINEL, AYŞE BERNA; GANİZ, MURAT CAN; ŞİPAL, BİLGE; ERKAYA, EREN CAN; YÜCEDAĞ, ONUR CAN; and DOĞAN, MUHAMMED ALİ (2019) "Word sense disambiguation using semantic kernels with class-based term values," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27: No. 4, Article 57. https://doi.org/10.3906/elk-1805-131
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss4/57