Unsupervised Korean Word Sense Disambiguation using CoreNet

Abstract

In this study, we investigated unsupervised learning based Korean word sense disambiguation (WSD) using CoreNet, a Korean lexical semantic network. To facilitate the application of WSD to practical natural language processing problems, a reasonable method is required to distinguish between sense candidates. We therefore performed coarse-grained Korean WSD studies while utilizing the hierarchical semantic categories of CoreNet to distinguish between sense candidates. In our unsupervised approach, we applied a knowledge-based model that incorporated a Markov random field and dependency parsing to the Korean language in addition to utilizing the semantic categories of CoreNet. Our experimental results demonstrate that the developed CoreNet based coarse-grained WSD technique exhibited an 80.9% accuracy on the datasets we constructed, and was proven to be effective for practical applications.