Korean Compound Noun Decomposition and Semantic Tagging System using User-Word Intelligent Network


KIPS Transactions on Software and Data Engineering, Vol. 19, No. 1, pp. 63-76, Jan. 2012
10.3745/KIPSTB.2012.19.1.63, Full Text:

Abstract

We propose a Korean compound noun semantic tagging system using statistical compound noun decomposition and semantic relation information extracted from a lexical semantic network(U-WIN) and dictionary definitions. The system consists of three phases including compound noun decomposition, semantic constraint, and semantic tagging. In compound noun decomposition, best candidates are selected using noun location frequencies extracted from a Sejong corpus, and re-decomposes noun for semantic constraint and restores foreign nouns. The semantic constraints phase finds possible semantic combinations by using origin information in dictionary and Naive Bayes Classifier, in order to decrease the computation time and increase the accuracy of semantic tagging. The semantic tagging phase calculates the semantic similarity between decomposed nouns and decides the semantic tags. We have constructed 40,717 experimental compound nouns data set from Standard Korean Language Dictionary, which consists of more than 3 characters and is semantically tagged. From the experiments, the accuracy of compound noun decomposition is 99.26%, and the accuracy of semantic tagging is 95.38% respectively.


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Cite this article
[IEEE Style]
Y. H. Lee, C. Y. Ock and E. B. Lee, "Korean Compound Noun Decomposition and Semantic Tagging System using User-Word Intelligent Network," KIPS Journal B (2001 ~ 2012) , vol. 19, no. 1, pp. 63-76, 2012. DOI: 10.3745/KIPSTB.2012.19.1.63.

[ACM Style]
Yong Hoon Lee, Cheol Young Ock, and Eung Bong Lee. 2012. Korean Compound Noun Decomposition and Semantic Tagging System using User-Word Intelligent Network. KIPS Journal B (2001 ~ 2012) , 19, 1, (2012), 63-76. DOI: 10.3745/KIPSTB.2012.19.1.63.