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Semantic clustering of relations between named entities

Abstract : Most research in Information Extraction concentrates on the extraction of relations from texts but less work has been done about their organization after their extraction. We present in this article a multi-level clustering method to group semantically equivalent relations: a first step groups relation instances with similar expressions to form clusters with high precision; a second step groups these initial clusters into larger semantic clusters using more complex semantic similarities. Experiments demonstrate that our multi-level clustering not only improves the scalability of the method but also improves clustering results by exploiting redundancy in each initial cluster.
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W. Wang, R. Besançon, Olivier Ferret, B. Grau. Semantic clustering of relations between named entities. International Conference on Natural Language Processing, NLP 2014, 2014, Warsaw, Poland. pp.358-370, ⟨10.1007/978-3-319-10888-9_36⟩. ⟨cea-01847293⟩

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