Paper: Confidence in Structured-Prediction Using Confidence-Weighted Models

ACL ID D10-1095
Title Confidence in Structured-Prediction Using Confidence-Weighted Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Confidence-Weighted linear classifiers (CW) and its successors were shown to perform well on binary and multiclass NLP prob- lems. In this paper we extend the CW ap- proach for sequence learning and show that it achieves state-of-the-art performance on four noun phrase chucking and named entity recog- nition tasks. We then derive few algorith- mic approaches to estimate the prediction’s correctness of each label in the output se- quence. We show that our approach provides a reliable relative correctness information as it outperforms other alternatives in ranking label-predictions according to their error. We also show empirically that our methods output close to absolute estimation of error. Finally, we show how to use this information to im- prove active learning.