2010 PracticalVeryLargeScaleCRFs

• (Lavergne et al., 2010) ⇒ Thomas Lavergne, Olivier Cappé, and François Yvon. (2010). “Practical Very Large Scale CRFs.” In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, (ACL 2010).

Subject Headings: CRF Training Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%22Practical+very+large+scale+CRFs%22+2010
• http://dl.acm.org/citation.cfm?doid=&preflayout=flat#citedby

Quotes

Abstract

Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate structural dependency between labels. Even for the simple linear-chain model, taking structure into account implies a number of parameters and a computational effort that grows quadratically with the cardinality of the label set. In this paper, we address the issue of training very large CRFs, containing up to hundreds output labels and several billion features. Efficiency stems here from the sparsity induced by the use of a l¹ penalty term. Based on our own implementation, we compare three recent proposals for implementing this regularization strategy. experiments demonstrate that very large CRFs can be trained efficiently and that very large models are able to improve the accuracy, while delivering compact parameter sets.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 PracticalVeryLargeScaleCRFs	Thomas Lavergne Olivier Cappé François Yvon			Practical Very Large Scale CRFs			http://www.aclweb.org/anthology-new/P/P10/P10-1052.pdf			2010