Unsupervised Korean Word Sense Disambiguation using CoreNet
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
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.