Abstract
This paper describes our effort to build a large-scale commonsense knowledge base in Korean by converting a pre-existing one in English, called ConceptNet. The English commonsense knowledge base is essentially a huge net consisting of concepts and relations. Triplets in the form of Concept-Relation-Concept in the net were extracted from English sentences collected from volunteers through a Web site, who were interested in entering commonsense knowledge. Our effort is an attempt to obtain its Korean version by utilizing a variety of language resources and tools. We not only employed a morphological analyzer and existing commercial machine translation software but also developed our own special-purpose translation and out-of-vocabulary handling methods. In order to handle ambiguity, we also devised a noisy concept filtering and concept generalization methods. Out of the 2.4 million assertions, i.e. triplets of concept-relation-concept, in the English ConceptNet, we generated about 200,000 Korean assertions so far. Based on our manual judgments of a 5% sample, the accuracy was 84.4%.
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Jung, Y., Lee, JY., Kim, Y., Park, J., Myaeng, SH., Rim, HC. (2007). Building a Large-Scale Commonsense Knowledge Base by Converting an Existing One in a Different Language. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_3
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DOI: https://doi.org/10.1007/978-3-540-70939-8_3
Publisher Name: Springer, Berlin, Heidelberg
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