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).

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Jun Suzuki, Akinori Fujino, Hideki Isozaki
• Keywords: EMNLP 2007, Hybrid Model, Natural Language Processing, Semi-supervised Structured Output, Unlabeled Data