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Conference Paper

Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences

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Altun, Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hofmann, T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Altun, Y., Johnson, M., & Hofmann, T. (2003). Loss Functions and Optimization Methods for Discriminative Learning of Label Sequences. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2003) (pp. 145-152). East Stroudsburg, PA, USA: ACL.

Abstract

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.