Active learning for HPSG parse selection
Jason Baldridge, Miles Osborne · 2003
We describe new features and algorithms for HPSG parse selection models and address the task of creating annotated material to train them.We evaluate the ability of several sample selection methods to reduce the number of annotated sentences necessary to achieve a given level of performance.Our best method achieves a 60% reduction in the amount of training material without any loss in accuracy.