Automatic structuring and retrieval of large text files
Communications of the ACM
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Acquiring disambiguation rules from text
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
FASTUS: a system for extracting information from text
HLT '93 Proceedings of the workshop on Human Language Technology
Introduction to information extraction
AI Communications
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This project is developing a trainable system that can extract meaning from texts in different domains (example: various Internet news-groups). The system does partial parsing based on a large dictionary containing approximately 150,000 words. The system assists the user in extracting a semantic network representation for each member of a set of training articles contained in some large database. Based on the user's training, the system forms statistical tables, a knowledge base, and a set of rules mirroring the user's actions. The system then generalizes these rules. Using statistically based semantic classification, the system applies these rules to new articles from the database for automatically building semantic networks.