Abstract:
International audience; 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 (...)
International audience; 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. (Read More)
Data mining |
Artificial intelligence |
Natural language processing |
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