2006 SemiSupervisedConditionalRandom

• (Jiao et al., 2006) ⇒ Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Greiner, and Dale Schuurmans. (2006). “Semi-supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling.” In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. doi:10.3115/1220175.1220202

Subject Headings: Semi-Supervised Learning, CRFs, Semi-Supervised CRF Training.

Cited By

• http://scholar.google.com/scholar?q=%22Semi-supervised+conditional+random+fields+for+improved+sequence+segmentation+and+labeling%22+2006
• http://dl.acm.org/citation.cfm?id=1220175.1220202&preflayout=flat#citedby

Quotes

Abstract

We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled and unlabeled training data. Our approach is based on extending the minimum entropy regularization framework to the structured prediction case, yielding a training objective that combines unlabeled conditional entropy with labeled conditional likelihood. Although the training objective is no longer concave, it can still be used to improve an initial model (e.g. obtained from supervised training) by iterative ascent. We apply our new training algorithm to the problem of identifying gene and protein mentions in biological texts, and show that incorporating unlabeled data improves the performance of the supervised CRF in this case.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 SemiSupervisedConditionalRandom	Dale Schuurmans Chi-Hoon Lee Shaojun Wang Russell Greiner Feng Jiao			Semi-supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling				10.3115/1220175.1220202		2006