Extracting hyponymy relations from domain-specific free texts

Chun Xia Zhang; Cun Gen Cao; Lei Liu; Zhen Dong Niu; Jun Hong Lin

doi:10.1109/ICMLC.2007.4370728

Extracting hyponymy relations from domain-specific free texts

Chun Xia Zhang^*, Cun Gen Cao, Lei Liu, Zhen Dong Niu, Jun Hong Lin

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Pages	3360-3365
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2007.4370728
Publication status	Published - 2007
Event	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China Duration: 19 Aug 2007 → 22 Aug 2007

Publication series

Name	Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
Volume	6

Conference

Conference	6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
Country/Territory	China
City	Hong Kong
Period	19/08/07 → 22/08/07

Keywords

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

Access to Document

10.1109/ICMLC.2007.4370728

Cite this

Zhang, C. X., Cao, C. G., Liu, L., Niu, Z. D., & Lin, J. H. (2007). Extracting hyponymy relations from domain-specific free texts. In Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007 (pp. 3360-3365). Article 4370728 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6). https://doi.org/10.1109/ICMLC.2007.4370728

@inproceedings{7f9a1e2aaad64de5806faeb4320ff6a8,

title = "Extracting hyponymy relations from domain-specific free texts",

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.",

keywords = "Boundary features of domain-specific terms, Domain-specific ontology, Hyponymy extraction, Pattern-matching conflict, Seed-driven learning",

author = "Zhang, {Chun Xia} and Cao, {Cun Gen} and Lei Liu and Niu, {Zhen Dong} and Lin, {Jun Hong}",

year = "2007",

doi = "10.1109/ICMLC.2007.4370728",

language = "English",

isbn = "142440973X",

series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

pages = "3360--3365",

booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",

note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",

}

Zhang, CX, Cao, CG, Liu, L, Niu, ZD & Lin, JH 2007, Extracting hyponymy relations from domain-specific free texts. in Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007., 4370728, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007, vol. 6, pp. 3360-3365, 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, Hong Kong, China, 19/08/07. https://doi.org/10.1109/ICMLC.2007.4370728

Extracting hyponymy relations from domain-specific free texts. / Zhang, Chun Xia; Cao, Cun Gen; Liu, Lei et al.
Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007. 2007. p. 3360-3365 4370728 (Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007; Vol. 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Extracting hyponymy relations from domain-specific free texts

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AU - Cao, Cun Gen

AU - Liu, Lei

AU - Niu, Zhen Dong

AU - Lin, Jun Hong

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N2 - 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.

AB - 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.

KW - Boundary features of domain-specific terms

KW - Domain-specific ontology

KW - Hyponymy extraction

KW - Pattern-matching conflict

KW - Seed-driven learning

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ER -

Extracting hyponymy relations from domain-specific free texts

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Keywords

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