Scientific and Technical (S and T) intelligence analysts consume huge amounts of data to understand how scientific
progress and engineering efforts affect current and future military capabilities. One of the most important types of
information S and T analysts exploit is the quantities discussed in their source material. Frequencies, ranges, size, weight,
power, and numerous other properties and measurements describing the performance characteristics of systems and the
engineering constraints that define them must be culled from source documents before quantified analysis can begin.
Automating the process of finding and extracting the relevant quantities from a wide range of S and T documents is
difficult because information about quantities and their units is often contained in unstructured text with ad hoc
conventions used to convey their meaning. Currently, even simple tasks, such as searching for documents discussing RF
frequencies in a band of interest, is a labor intensive and error prone process. This research addresses the challenges
facing development of a document processing capability that extracts quantities and units from S and T data, and how
Natural Language Processing algorithms can be used to overcome these challenges.