NDLI: Discover hierarchical lexical hyponymy relation from large-scale concept set

Content Provider	IEEE Xplore Digital Library
Author	Jiayu Zhou Youfang Lin Shi Wang Cungen Cao
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing (Jiayu Zhou; Youfang Lin)
Abstract	Lexical hyponymy relation is a kind of hyponymy that can be directly inferred from the lexical compositions of concepts, and of great importance in ontology learning. However, there is a key problem that the lexical hyponymy is so commonsensible that it cannot be discovered by any existing acquisition methods. In this paper, we propose a novel approach to semi-automatically discover hierarchical lexical hyponymy relations from a large-scale concept set, instead of analyzing lexical structures of concepts. Firstly we design Common Suffix Tree (CST) to cluster lexical concepts. We extract class concepts candidates from CST by statistic-base rules that we investigated, and then use a Google-based classifier to verify them. Finally, we extract lexical hyponymy relation candidates from CST and judge them after a prefix clustering process. Experimental result shows us that our approach can correctly discover most lexical hyponymy relations in a given large-scale concept set.
Starting Page	205
Ending Page	214
File Size	916137
Page Count	10
File Format	PDF
ISBN	9781424425389
DOI	10.1109/COGINF.2008.4639170
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Suffix Probability Inflexion Rule Knowledge Acquisition Lexical Hyponymy Relation Information Extraction Common Suffix Tree
Content Type	Text
Resource Type	Article

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