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- W2130010963 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." @default.
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- W2130010963 date "2007-01-01" @default.
- W2130010963 modified "2023-09-23" @default.
- W2130010963 title "Extracting Hyponymy Relations from Domain-Specific Free Texts" @default.
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- W2130010963 doi "https://doi.org/10.1109/icmlc.2007.4370728" @default.
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