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Unsupervised relation extraction by massive clustering
González Pellicer, Edgar; Turmo Borras, Jorge
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
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 learningprocess. 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.
2012-05-10
Àrees temàtiques de la UPC::Enginyeria electrònica i telecomunicacions::Processament del senyal::Processament de la parla i del senyal acústic
Data mining -- Data processing
Information retrieval
Text analysis
Pattern clustering
Mineria de dades
Open Access
Conference Object
         

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