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Coupling Semi-Supervised Learning of Categories and Relations

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha.

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Andrew Carlson, Justin Betteridge, Estevam R. Hruschka Jr. and Tom M. Mitchell. 2009. Coupling semi-supervised learning of categories and relations. In Proceedings of the NAACL -HLT-09 Workshop on Semi-Supervised Learning for Natural Language Processing.

Abstract: We consider semi-supervised learning of information extraction methods, especially for extracting instances of noun categories (e.g., ‘athlete’, ‘team’) and relations (e.g., ‘playsForTeam(athlete,team)’). Semi-supervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless incorrect set of extractions. We propose that this problem can be overcome by simultaneously learning classifiers for many different categories and relations in the presence of an ontology defining constraints that couple the training of these classifiers. Experimental results show that simultaneously learning a coupled collection of classifiers for 30 categories and relations results in much more accurate extractions than training classifiers individually.

This talk is part of the Natural Language Processing Reading Group series.

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