Contextual word similarity and estimation from sparse data
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
The NYU system for MUC-6 or where's the syntax?
MUC6 '95 Proceedings of the 6th conference on Message understanding
SRA: description of the SRA system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Information extraction with automatic knowledge expansion
Information Processing and Management: an International Journal
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Information Extraction (IE) systems today are commonly based on pattern matching. The patterns are regular expressions stored in a customizable knowledge base. Adapting an IE system to a new subject domain entails the construction of a new pattern base --- a time-consuming and expensive task. We describe a strategy for building patterns from examples. To adapt the IE system to a new domain quickly, the user chooses a set of examples in a training text, and for each example gives the logical form entries which the example induces. The system transforms these examples into patterns and then applies meta-rules to generalize these patterns.