Semantic clustering of relations between named entities

W. Wang (1) , R. Besançon (1) , Olivier Ferret (1) , B. Grau (2)

1 DIASI (CEA, LIST) - Département Intelligence Ambiante et Systèmes Interactifs
2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

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.

Mots clés: Information analysis; Information retrieval; Natural language processing systems; Unsupervised information extractions; Semantic similarity; Semantic clustering; Equivalent relation; Clustering results; Clustering methods; Clustering algorithms; Semantics; unsupervised information extraction; relation extraction; clustering

Domaines: Informatique [cs]

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⟩