Statistical confidence measures for probabilistic parsing.
We introduce a formal framework that allows the cal- culation of new purely statistical conﬁdence measures for parsing, which are estimated from posterior proba- bility of constituents. These measures allow us to mark each constituent of a parse tree as correct or incor- rect. Experimental assessment using the Penn Tree- bank shows favorable results for the classical conﬁ- dence evaluation metrics: the CER and the ROC curve. We also present preliminar experiments on application of conﬁdence measures to improve parse trees by au- tomatic constituent relabeling.