Paper: Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields

ACL ID P08-1099
Title Generalized Expectation Criteria for Semi-Supervised Learning of Conditional Random Fields
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

This paper presents a semi-supervised train- ing method for linear-chain conditional ran- dom fields that makes use of labeled features rather than labeled instances. This is accom- plished by using generalized expectation cri- teria to express a preference for parameter set- tings in which the model’s distribution on un- labeled data matches a target distribution. We induce target conditional probability distribu- tions of labels given features from both anno- tated feature occurrences in context and ad- hoc feature majority label assignment. The use of generalized expectation criteria allows for a dramatic reduction in annotation time by shifting from traditional instance-labeling to feature-labeling, and the methods presented outperform traditional CRF training and other semi-supervis...