Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reranking and self-training for parser adaptation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A progressive feature selection algorithm for ultra large feature spaces
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Effective use of prosody in parsing conversational speech
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Word-Level Confidence Estimation for Machine Translation
Computational Linguistics
Automatic selection of high quality parses created by a fully unsupervised parser
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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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 with a subtree error rate of 41.91%, a closely related benchmark with the Charniak parser from (Kahn et al., 2005).