Foundations of statistical natural language processing
Foundations of statistical natural language processing
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
PCFG models of linguistic tree representations
Computational Linguistics
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
New developments in parsing technology
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We present a semiparametric generative model for supervised learning with structured outputs. The main algorithmic idea is to replace the parameters of an underlying generative model (such as a stochastic grammars) with input-dependent predictions obtained by (kernel) logistic regression. This method avoids the computational burden associated with the comparison between target and predicted structure during the training phase, but requires as an additional input a vector of sufficient statistics for each training example. The resulting training algorithm is asymptotically more efficient than structured output SVM as the size of the output structure grows. At the same time, by computing parameters of a joint distribution as a function of the full input structure, typical expressiveness limitations of related conditional models (such as maximum entropy Markov models) can be potentially avoided. Empirical results on artificial and real data (in the domains of natural language parsing and RNA secondary structure prediction) show that the method works well in practice and scales up with the size of the output structures.