Communications of the ACM
An empirical study of automated dictionary construction for information extraction in three domains
Artificial Intelligence - Special volume on empirical methods
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
The use of word sense disambiguation in an information extraction system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A WordNet Based Rule Generalization Engine for Meaning Extraction System
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Learning and generalization in the creation of information extraction systems
Learning and generalization in the creation of information extraction systems
Overview of the fourth message understanding evaluation and conference
MUC4 '92 Proceedings of the 4th conference on Message understanding
New York University PROTEUS system: MUC-4 test results and analysis
MUC4 '92 Proceedings of the 4th conference on Message understanding
The role of wordnet in the creation of a trainable message understanding system
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
The use of word sense disambiguation in an information extraction system
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning rules for information extraction
Natural Language Engineering
Adaptive information extraction
ACM Computing Surveys (CSUR)
Enabling information extraction by inference of regular expressions from sample entities
Proceedings of the 20th ACM international conference on Information and knowledge management
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In a user-trained information extraction system, the cost of creating the rules for information extraction can be greatly reduced by maximizing the effectiveness of user inputs. If the user specifies one example of a desired extraction, our system automatically tries a variety of generalizations of this rule including generalizations of the terms and permutations of the ordering of significant words. Where modifications of the rules are successful, those rules are incorporated into the extraction set. The theory of such generalizations and a measure of their usefulness is described.