2002 CombiningSampleSelectionandErro

• (Ng & Cardie, 2002) ⇒ Vincent Ng, and Claire Cardie. (2002). “Combining Sample Selection and Error-driven Pruning for Machine Learning of Coreference Rules.” In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10. doi:10.3115/1118693.1118701

Subject Headings: Classification-based Coreference Resolution System.

Cited By

• http://scholar.google.com/scholar?q=%222002%22+Combining+Sample+Selection+and+Error-driven+Pruning+for+Machine+Learning+of+Coreference+Rules
• http://dl.acm.org/citation.cfm?id=1118693.1118701&preflayout=flat#citedby

Quotes

Abstract

Most machine learning solutions to noun phrase coreference resolution recast the problem as a classification task. We examine three potential problems with this reformulation, namely, skewed class distributions, the inclusion of " hard " training instances, and the loss of transitivity inherent in the original coreference relation. We show how these problems can be handled via intelligent sample selection and error-driven pruning of classification rule-sets. The resulting system achieves an F-measure of 69.5 and 63.4 on the MUC-6 and MUC-7 coreference resolution data sets, respectively, surpassing the performance of the best MUC-6 and MUC-7 coreference systems. In particular, the system outperforms the best-performing learning-based coreference system to date.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 CombiningSampleSelectionandErro	Vincent Ng Claire Cardie			Combining Sample Selection and Error-driven Pruning for Machine Learning of Coreference Rules				10.3115/1118693.1118701		2002