Ontology learning from text is considered as an appealing and challeging alternative to address the shortcomings of the hand-crafted ontologies. In this paper, we present OLea, a new framework for ontology learning from text. The proposal is a hybrid approach combining the pattern-based and the distributionnal approaches. It addresses key issues in the area of ontology learning: context-dependency, low recall of the pattern-based approach, low precision of the distributionnal approach, and finally ontology evolution. Experiments performed at each stage of the learning process show the advantages and drawbacks of the proposal.
CITATION STYLE
El Sayed, A., & Hacid, H. (2008). A hybrid approach for taxonomy learning from text. In COMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium (pp. 255–266). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-7908-2084-3_21
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