Confidence Measures for Error Discrimination in an Interactive Predictive Parsing Framework

We study the use of Confidence Measures (CM) for erroneous constituent discrimination in an Interactive Predictive Parsing (IPP) framework. The IPP framework allows 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 automatic parser). We show that CMs can help in detecting erroneous constituents more quickly through all the IPP process. We present two methods for precalculating the confidence threshold (globally and per-interaction), and observe that CMs remain highly discriminant as the IPP process advances.