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.
@InProceedings{HAN18.224, author = {Kijong Han and Sangha Nam and Jiseong Kim and Younggyun Hahm and KEY-SUN CHOI}, title = "{Unsupervised Korean Word Sense Disambiguation using CoreNet}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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