Computing Confidence Scores for All Sub Parse Trees

Computing confidence scores for applications, such as dialogue system, information retrieving and extraction, is an active research area. However, its focus has been primarily on computing word-, concept-, or utterance-level confidences. Motivated by the need from sophisticated dialogue systems for more effective dialogs, we generalize the confidence annotation to all the subtrees, the first effort in this line of research. The other contribution of this work is that we incorporated novel long distance features to address challenges in computing multi-level confidence scores. Using Conditional Maximum Entropy (CME) classifier with all the selected features, we reached an annotation error rate of 26.0% in the SWBD corpus, compared

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Feng Lin, Fuliang Weng
• Keywords: ACL 2008, Computational Linguistics, Confidence Scores, Dialogue Systems, Multi-level Confidence Scores