NDLI: Extracting Hyponymy Relations from Domain-Specific Free Texts

Content Provider	IEEE Xplore Digital Library
Author	Chun-Xia Zhang Cun-Gen Cao Lei Liu Zhen-Dong Niu Jun-Hong Lin
Copyright Year	2007
Description	Author affiliation: Beijing Inst. of Technol., Beijing (Chun-Xia Zhang)
Abstract	Domain-specific ontologies have shown their powerful usefulness in many application areas, such as semantic web, information sharing, and natural language processing. However, manually building of domain ontologies still remains a tedious and cumbersome task. Hyponymy is a core component of domain-specific ontologies. In this paper, we propose three symbolic learning methods, which are integrated together to extract hyponymies from un-annotated domain-specific Chinese free texts. The three symbolic learning methods include seed-driven learning, pattern-mediated learning, and term composition based learning. Experimental results show that the algorithm is adequate to extracting the hyponymies from unstructured domain-specific Chinese corpus.
Starting Page	3360
Ending Page	3365
File Size	890504
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ontologies Learning systems Data mining Semantic Web Machine learning Cybernetics Natural language processing Pattern matching Computer science Application software Pattern-matching conflict Hyponymy extraction Domain-specific ontology Seed-driven learning Boundary features of domain-specific terms
Content Type	Text
Resource Type	Article

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