A maximum entropy approach to natural language processing
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised conditional random fields for improved sequence segmentation and labeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Maximum entropy density estimation and modeling geographic distributions of species
Maximum entropy density estimation and modeling geographic distributions of species
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning from measurements in exponential families
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Dependency grammar induction via bitext projection constraints
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Active learning by labeling features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Minimally-supervised extraction of entities from text advertisements
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Constraint-driven rank-based learning for information extraction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Sparsity in dependency grammar induction
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
Learning tractable word alignment models with complex constraints
Computational Linguistics
Posterior Sparsity in Unsupervised Dependency Parsing
The Journal of Machine Learning Research
Rich prior knowledge in learning for NLP
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Feature-rich part-of-speech tagging for morphologically complex languages: application to Bulgarian
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Unified expectation maximization
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Collective information extraction with context-specific consistencies
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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We present an objective function for learning with unlabeled data that utilizes auxiliary expectation constraints. We optimize this objective function using a procedure that alternates between information and moment projections. Our method provides an alternate interpretation of the posterior regularization framework (Graca et al., 2008), maintains uncertainty during optimization unlike constraint-driven learning (Chang et al., 2007), and is more efficient than generalized expectation criteria (Mann & McCallum, 2008). Applications of this framework include minimally supervised learning, semi-supervised learning, and learning with constraints that are more expressive than the underlying model. In experiments, we demonstrate comparable accuracy to generalized expectation criteria for minimally supervised learning, and use expressive structural constraints to guide semi-supervised learning, providing a 3%-6% improvement over state-of-the-art constraint-driven learning.