A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Conditioning probabilistic databases
Proceedings of the VLDB Endowment
Efficiently incorporating user feedback into information extraction and integration programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Bayesian knowledge corroboration with logical rules and user feedback
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Large-scale cross-document coreference using distributed inference and hierarchical models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A discriminative hierarchical model for fast coreference at large scale
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Trust, but verify: predicting contribution quality for knowledge base construction and curation
Proceedings of the 7th ACM international conference on Web search and data mining
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Knowledge bases (KB) provide support for real-world decision making by exposing data in a structured format. However, constructing knowledge bases requires gathering data from many heterogeneous sources. Manual efforts for this task are accurate, but lack scalability, and automated approaches provide good coverage, but are not reliable enough for real-world decision makers to trust. These two approaches to KB construction have complementary strengths: in this paper we propose a novel framework for supporting humanproposed edits to knowledge bases.