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Title:
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Multilingual ontology acquisition from multiple MRDs.
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Author:
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Nichols, Eric.; Bond, Francis.; Tanaka, Takaaki.; Fujita, Sanae.; Flickinger, Dan.
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Copyright year:
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2006 |
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Abstract:
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In this paper, we outline the development of a system that automatically constructs ontologies by extracting knowledge
from dictionary definition sentences using Robust Minimal Recursion Semantics (RMRS). Combining deep and shallow
parsing resource through the common formalism of RMRS allows us to extract ontological relations in greater quantity and
quality than possible with any of the methods independently. Using this method, we construct ontologies from two different
Japanese lexicons and one English lexicon. We then link them to existing, handcrafted ontologies, aligning them at the
word-sense level. This alignment provides a representative evaluation of the quality of the relations being extracted. We
present the results of this ontology construction and discuss how our system was designed to handle multiple lexicons and languages. |
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Subject:
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DRNTU::Humanities::Linguistics::Sociolinguistics::Computational linguistics. |
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Type:
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Conference Paper |
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Conference name:
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Proceedings of the 2nd Workshop on Ontology Learning and Population (2006:Sydney) |
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School:
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College of Humanities, Arts, and Social Sciences |
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Rights:
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© 2006 Association for Computational Linguistics. This paper was published in Proceedings of the 2nd Workshop on Ontology Learning and Population and is made available as an electronic reprint (preprint) with permission of Association for Computational Linguistics. The paper can be found at the following official URL: http://www.aclweb.org/anthology/W/W06/W06-0502.pdf. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. |
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Version:
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Published version |
