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EMNLP
2007

Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach

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Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach. We define the objective function of our hybrid model, which is written in log-linear form, by discriminatively combining discriminative structured predictor(s) with generative model(s) that incorporate unlabeled data. Then, unlabeled data is used in a generative manner to increase the sum of the discriminant functions for all outputs during the parameter estimation. Experiments on named entity recognition (CoNLL-2003) and syntactic chunking (CoNLL-2000) data show that our hybrid model significantly outperforms the stateof-the-art performance obtained with supervised SOL methods, such as conditional random fields (CRFs).
Jun Suzuki, Akinori Fujino, Hideki Isozaki
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Jun Suzuki, Akinori Fujino, Hideki Isozaki
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