PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Confidence Measures for Error Discrimination in an Interactive Predictive Parsing Framework
Ricardo Sánchez-Sáez, Joan Andreu Sánchez and José Miguel Benedí
In: COLING 2010, August 23-27, Beijing, China.

Abstract

We study the use of Confidence Measures (CM) for erroneous constituent discrimination in an Interactive Predictive Parsing (IPP) framework. The IPP framework al- lows to build interactive tree annotation systems that can help human correctors in constructing error-free parse trees with little effort (compared to manually post-editing the trees obtained from an auto- matic parser). We show that CMs can help in detecting erroneous constituents more quickly through all the IPP process. We present two methods for precalculat- ing the confidence threshold (globally and per-interaction), and observe that CMs remain highly discriminant as the IPP process advances.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7447
Deposited By:Alfons Juan
Deposited On:17 March 2011