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Learning Hierarchical Lexical Hyponymy

Learning Hierarchical Lexical Hyponymy

Jiayu Zhou, Shi Wang, Cungen Cao
Copyright: © 2010 |Volume: 4 |Issue: 1 |Pages: 17
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781616929312|EISSN: 1557-3966|DOI: 10.4018/jcini.2010010106
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MLA

Zhou, Jiayu, et al. "Learning Hierarchical Lexical Hyponymy." IJCINI vol.4, no.1 2010: pp.98-114. http://doi.org/10.4018/jcini.2010010106

APA

Zhou, J., Wang, S., & Cao, C. (2010). Learning Hierarchical Lexical Hyponymy. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 4(1), 98-114. http://doi.org/10.4018/jcini.2010010106

Chicago

Zhou, Jiayu, Shi Wang, and Cungen Cao. "Learning Hierarchical Lexical Hyponymy," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 4, no.1: 98-114. http://doi.org/10.4018/jcini.2010010106

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Abstract

Chinese information processing is a critical step toward cognitive linguistic applications like machine translation. Lexical hyponymy relation, which exists in some Eastern languages like Chinese, is a kind of hyponymy that can be directly inferred from the lexical compositions of concepts, and of great importance in ontology learning. However, a key problem is that the lexical hyponymy is so commonsense that it cannot be discovered by any existing acquisition methods. In this paper, we systematically define lexical hyponymy relationship, its linguistic features and propose a computational approach to semi-automatically learn hierarchical lexical hyponymy relations from a large-scale concept set, instead of analyzing lexical structures of concepts. Our novel approach discovered lexical hyponymy relation by examining statistic features in a Common Suffix Tree. The experimental results show that our approach can correctly discover most lexical hyponymy relations in a given large-scale concept set.

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