Maximum expected F-measure training of logistic regression models

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.