. 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. 1 Introduction The tremendous topics available on Internet give rise to the demand for an easily adaptable meaning extraction system for dif...

This paper presents an information extraction system that processes the textual content of classified newspaper advertisements in French. The system uses both lexical (words, regular expressions) and contextual information to structure the content of the ads on the basis of predefined thematic forms. The paper first describes the enhanced tagging mechanism used for extraction. A quantitative evaluation of the system is then provided: scores of 99.0% precision/99.8% recall for domain identification and 73% accuracy for information extraction were achieved, on the basis of a comparison with human annotators.