PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Statistical confidence measures for probabilistic parsing.
Ricardo Sánchez-Sáez, Joan Andreu Sánchez and José Miguel Benedí
In: International Conference on Recent Advances in Natural Language Processing (RANLP '09)(2009).

Abstract

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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5855
Deposited By:Alfons Juan
Deposited On:08 March 2010