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.
CITATION STYLE
Wang, W., Besançon, R., Ferret, O., & Grau, B. (2014). Semantic clustering of relations between named entities. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8686, 358–370. https://doi.org/10.1007/978-3-319-10888-9_36
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