A WordNet based rule generalization engine in meaning extraction system

This paper presents a rule based methodology for efficiently creating meaning extraction systems. The methodology allows a user to scan sample texts in a domain to be processed and to create meaning extraction rules that specifically address his or her needs. Then it automatically generalizes the rules using the power of the WordNet system so that they can effectively extract a broad class of information even though they were based on extraction from a few very specific articles. Finally, the generalized rules can be applied to large databases of text to do the translation that will extract the particular information the user desires. A recently developed mechanism is presented that uses the strategy of over-generalizing to achieve high recall (with low precision) and then selectively specializing to bring the precision up to acceptable levels.