Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
From information retrieval to information extraction
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Reducing labeling effort for structured prediction tasks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
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Information extraction systems have been successfully deployed for domains ranging from terrorist activities to medical records. However, building these systems remains costly for users who lack annotated training corpora or knowledge engineering expertise. This paper proposes a framework for an interactive information extraction environment in which the user trains the system by example and by feedback about performance. If successful, this will be the first system that allows end-users to create information extraction systems without the aid of computational linguists and NLP system designers.