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oai:recercat.cat:2117/92292026-01-30T06:58:54Zcom_2072_1033col_2072_452950

Unsupervised relation extraction by massive clustering González Pellicer, Edgar Turmo Borras, Jorge Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic Data mining -- Data processing Information retrieval Text analysis Pattern clustering Mineria de dades The goal of Information Extraction is to automatically generate structured pieces of information from the relevant information contained in text documents. Machine Learning techniques have been applied to reduce the cost of Information Extraction system adaptation. However, elements of human supervision strongly bias the learning process. Unsupervised learning approaches can avoid these biases. In this paper, we propose an unsupervised approach to learning for Relation Detection, based on the use of massive clustering ensembles. The results obtained on the ACE Relation Mention Detection task outperform in terms of F1 score by 5 points the state of the art of unsupervised techniques for this evaluation framework, in addition to being simpler and more flexible. Peer Reviewed Postprint (published version) 2009 Conference report González, E.; Turmo, J. Unsupervised relation extraction by massive clustering. A: IEEE International Conference On Data Mining. "9th IEEE International Conference On Data Mining". Miami: 2009, p. 782-787. https://hdl.handle.net/2117/9229 10.1109/ICDM.2009.81 eng http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5360311&queryText%3Dgonz%C3%A0lez+icdm+2009%26openedRefinements%3D*%26searchField%3DSearch+All Open Access 6 p. application/pdf