EXTRACTING IMPLICIT INFORMATION FROM FREE TEXT TECHNICAL REPORTS

We explore the issue of extracting both explicit and implicit information from narrative technical reports through knowledge-based free text understanding. We rely on the assumption that whereas technical texts convey much implicit information, such information can be recovered through natural language analysis by building and reasoning on a model of the situation described, if both linguistic and detailed world knowledge are provided to the system. We evaluated the feasibility of this approach by designing and testing a prototype performing information extraction from clinical record sentences in a restricted medical domain: thyroid cancer care. This prototype was fully implemented and was tested on actual sentences. We present the natural language processing strategy adopted in our system with emphasis on knowledge use, as well as the preliminary results obtained.