A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Practical structured learning techniques for natural language processing
Practical structured learning techniques for natural language processing
Piecewise training for structured prediction
Machine Learning
Syntactic processing using the generalized perceptron and beam search
Computational Linguistics
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
An overview of statistical learning theory
IEEE Transactions on Neural Networks
On amortizing inference cost for structured prediction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We show that the recently proposed piecewise approximation approach can benefit conditional random fields estimation using the structured perceptron algorithm. We present experiments in noun-phrase chunking task on the CoNLL-2000 corpus. The results show that, compared to standard training, applying the piecewise approach during model estimation may yield not only savings in training time but also improvement in model performance on test set due to added model regularization.