Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Discriminative Framework for Modelling Object Classes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
The asymptotics of semi-supervised learning in discriminative probabilistic models
Proceedings of the 25th international conference on Machine learning
Training conditional random fields using incomplete annotations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Efficient computation of entropy gradient for semi-supervised conditional random fields
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Semi-Supervised Sequence Labeling with Self-Learned Features
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Semi-supervised active learning for sequence labeling
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 2 - Volume 2
An empirical study of semi-supervised structured conditional models for dependency parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Semi-Supervised Learning
Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models
Pattern Recognition Letters
| Hi-index | 0.00 |
Conditional random fields are among the state-of-the art approaches to structured output prediction, and the model has been adopted for various real-world problems. The supervised classification is expensive, since it is usually expensive to produce labelled data. Unlabeled data are relatively cheap, but how to use it? Unlabeled data can be used to estimate marginal probability of observations, and we exploit this idea in our work. Introduction of unlabeled data and of probability of observations into a purely discriminative model is a challenging task. We consider an extrapolation of a recently proposed semi-supervised criterion to the model of conditional random fields, and show its drawbacks. We discuss alternative usage of the marginal probability and propose a pool-based active learning approach based on quota sampling. We carry out experiments on synthetic as well as on standard natural language data sets, and we show that the proposed quota sampling active learning method is efficient.