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

ACL ID D07-1083
Title Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discrim- inative approach. We define the objective function of our hybrid model, which is writ- ten in log-linear form, by discriminatively combining discriminative structured predic- tor(s) with generative model(s) that incor- porate unlabeled data. Then, unlabeled data is used in a generative manner to in- crease the sum of the discriminant functions for all outputs during the parameter estima- tion. Experiments on named entity recogni- tion (CoNLL-2003) and syntactic chunking (CoNLL-2000) data show that our hybrid model significantly outperforms the state- of-the-art performance obtained with super- vised SOL methods, such as conditio...