Paper: Maximum Expected F-Measure Training Of Logistic Regression Models

ACL ID H05-1087
Title Maximum Expected F-Measure Training Of Logistic Regression Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

We consider the problem of training logis- tic regression models for binary classifi- cation in information extraction and infor- mation retrieval tasks. Fitting probabilis- tic 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 proce- dure based on empirical risk minimiza- tion / utility maximization and evaluate it on a simple extraction task.