Extracting hyponymy relations from domain-specific free texts

被引：0

作者：

Zhang, Chun-Xia ^{[1
]}

Cao, Cun-Gen ^{[2
]}

Liu, Lei ^{[2
]}

Niu, Zhen-Dong ^{[1
]}

Lin, Jun-Hong ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China

[2] Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China

来源：

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2007年

关键词：

hyponymy extraction; domain-specific ontology; seed-driven learning; boundary features of domain-specific terms; pattern-matching conflict;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：3360 / +

页数：2