T
his Perspective offers a collection of lessons learned from RAND
Corporation projects that employed natural language processing (NLP)
tools and methods. It is written as a reference document for the practitioner
and is not intended to be a primer on concepts, algorithms, or applications,
nor does it purport to be a systematic inventory of all lessons relevant to NLP or
data analytics. It is based on a convenience sample of NLP practitioners who spend
or spent a majority of their time at RAND working on projects related to national
defense, national intelligence, international security, or homeland security; thus, the
lessons learned are drawn largely from projects in these areas. Although few of the
lessons are applicable exclusively to the U.S. Department of Defense (DoD) and its
NLP tasks, many may prove particularly salient for DoD, because its terminology is
very domain-specific and full of jargon, much of its data are classified or sensitive,
its computing environment is more restricted, and its information systems are gen-
erally not designed to support large-scale analysis. This Perspective addresses each
PETER SCHIRMER, AMBER JAYCOCKS, SEAN MANN, WILLIAM MARCELLINO, LUKE J. MATTHEWS, JOHN DAVID PARSONS,
DAVID SCHULKER
Natural Language Processing
Security- and Defense-Related Lessons Learned
C O R P O R A T I O N
Perspective
EXPERT INSIGHTS ON A TIMELY POLICY ISSUE
July 2021
