Comparing human and machine performance for natural language information extraction

Abstract

This paper presents results from a study comparing human performance on the text of natural language information extraction with that of machine extraction systems that were developed as part of the ARPA Tipster program. Information extraction is shown to be a difficult task for both humans and machines. Evidence for one set of text material, English Microelectronics, indicated that a human analyst produces about half the errors as does machine systems.