A maximum entropy approach to natural language processing
Computational Linguistics
The maximum entropy approach and probabilistic IR models
ACM Transactions on Information Systems (TOIS)
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Identifying and tracking entity mentions in a maximum entropy framework
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
A maximum entropy model for prepositional phrase attachment
HLT '94 Proceedings of the workshop on Human Language Technology
Training conditional random fields with multivariate evaluation measures
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A Generalization of Forward-Backward Algorithm
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Softmax-margin CRFs: training log-linear models with cost functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Boundary detection using f-measure-, filter- and feature- (F3) boost
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
The impact of language models and loss functions on repair disfluency detection
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
F-measure as the error function to train neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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We consider the problem of training logistic regression models for binary classification in information extraction and information retrieval tasks. Fitting probabilistic models for use with such tasks should take into account the demands of the task-specific utility function, in this case the well-known F-measure, which combines recall and precision into a global measure of utility. We develop a training procedure based on empirical risk minimization / utility maximization and evaluate it on a simple extraction task.