Learning to Parse Natural Language with Maximum Entropy Models
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An Algorithm that Learns What‘s in a Name
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An Alternate Objective Function for Markovian Fields
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Discriminative Reranking for Natural Language Parsing
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Maximum Entropy Markov Models for Information Extraction and Segmentation
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Estimators for stochastic "Unification-Based" grammars
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Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Contrastive estimation: training log-linear models on unlabeled data
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Training conditional random fields with multivariate evaluation measures
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Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
A cost sensitive part-of-speech tagging: differentiating serious errors from minor errors
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Discriminative models have been of interest in the NLP community in recent years. Previous research has shown that they are advantageous over generative models. In this paper, we investigate how different objective functions and optimization methods affect the performance of the classifiers in the discriminative learning framework. We focus on the sequence labelling problem, particularly POS tagging and NER tasks. Our experiments show that changing the objective function is not as effective as changing the features included in the model.