Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Unsupervised learning of field segmentation models for information extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Search-based structured prediction
Machine Learning
Enumerative Combinatorics: Volume 1
Enumerative Combinatorics: Volume 1
Guest editorial: special issue on structured prediction
Machine Learning
Proceedings of the 10th annual joint conference on Digital libraries
Structured output ordinal regression for dynamic facial emotion intensity prediction
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Dynamic joint sentiment-topic model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Conditional ordinal random fields for structured ordinal-valued label prediction
Data Mining and Knowledge Discovery
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Conditional random fields are one of the most popular structured prediction models. Nevertheless, the problem of incorporating domain knowledge into the model is poorly understood and remains an open issue. We explore a new approach for incorporating a particular form of domain knowledge through generalized isotonic constraints on the model parameters. The resulting approach has a clear probabilistic interpretation and efficient training procedures. We demonstrate the applicability of our framework with an experimental study on sentiment prediction and information extraction tasks.