Paper: Investigating Loss Functions And Optimization Methods For Discriminative Learning Of Label Sequences

ACL ID W03-1019
Title Investigating Loss Functions And Optimization Methods For Discriminative Learning Of Label Sequences
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

Discriminative models have been of inter- est in the NLP community in recent years. Previous research has shown that they are advantageous over generative mod- els. In this paper, we investigate how dif- ferent objective functions and optimiza- tion methods affect the performance of the classifiers in the discriminative learning framework. We focus on the sequence la- belling problem, particularly POS tagging and NER tasks. Our experiments show that changing the objective function is not as effective as changing the features in- cluded in the model.