Statistics and Computing
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Confidence estimation for NLP applications
ACM Transactions on Speech and Language Processing (TSLP)
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Confidence in structured-prediction using confidence-weighted models
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Open language learning for information extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Monte Carlo MCMC: efficient inference by approximate sampling
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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The purpose of this paper is to begin a conversation about the importance and role of confidence estimation in knowledge bases (KBs). KBs are never perfectly accurate, yet without confidence reporting their users are likely to treat them as if they were, possibly with serious real-world consequences. We define a notion of confidence based on the probability of a KB fact being true. For automatically constructed KBs we propose several algorithms for estimating this confidence from pre-existing probabilistic models of data integration and KB construction. In particular, this paper focuses on confidence estimation in entity resolution. A goal of our exposition here is to encourage creators and curators of KBs to include confidence estimates for entities and relations in their KBs.