2009 UnsupervisedRelationExtrac

• (Gonzalez & Turmo, 2009) ⇒ Edgar González, Jordi Turmo. (2009). “Unsupervised Relation Extraction by Massive Clustering.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.81

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Abstract

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 UnsupervisedRelationExtrac	Edgar González Jordi Turmo			Unsupervised Relation Extraction by Massive Clustering		ICDM 2009 Proceedings		10.1109/ICDM.2009.81		2009