EXTRACTING HYPONYMY RELATIONS FROM DOMAIN-SPECIFIC FREE TEXTS

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.

关键词： Hyponymy extraction Domain-specific ontology Seed-driven learning Boundary features of domain-specific terms Pattern-matching conflict

作者: CHUN-XIA ZHANG CUN-GEN CAO LEI LIU ZHEN-DONG NIU JUN-HONG LIN

作者单位: School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3360-3365

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）