MetLife processes over 300,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult since they include many free-form text fields. MITA, MetLife’s Intelligent Text Analyzer, uses the Information Extraction --IE-- technique of Natural Language Processing to structure the extensive textual fields on a Life Insurance Application. Knowledge engineering, with the help of underwriters as domain experts, was performed to elicit significant concepts for both medical and occupational textual fields. A corpus of 20,000 Life Insurance Applications provided the syntactical and semantic patterns in which these underwriting concepts occur. These patterns, in conjunction with the concepts, formed the frameworks for IE. Extension of the IE work developed by Lehnert was used to populate these frameworks with classes obtained from SNOMED and DOT ontologies. Thee structured frameworks can then be analyzed by conventional knowledge based systems. We project that MITA and knowledge based analyzers will increase underwriting productivity by 20 to 30%.