Data-Dependent Regularization

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.

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Data-Dependent Regularization

Cited by 6 publications

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Data heterogeneity consideration in semi-supervised learning

Data heterogeneity consideration in semi-supervised learning

A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes

Semi-supervised conditional random fields for improved sequence segmentation and labeling

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