Natural Language Processing: Security- and Defense-Related Lessons Learned

Abstract

This reference document offers a collection of lessons learned by practitioners from RAND Corporation projects that employed natural language processing (NLP) tools and methods. NLP is an umbrella term for the range of tools and methods that enable computers to analyze human language. The descriptions of lessons learned are organized around four steps: data collection, data processing (i.e., NLP-specific text processing in preparation for modeling), modeling, and application development and deployment. The research reported here was completed in February 2021 and underwent security review with the sponsor and the Defense Office of Prepublication and Security Review before public release.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2021
Accession Number
AD1143662

Entities

People

  • Amber Jaycocks
  • David Schulker
  • John D. Parsons
  • Luke J. Matthews
  • Peter Schirmer
  • Sean Mann
  • William M. Marcellino

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Autonomy
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Software
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Languages
  • Computers
  • Data Processing
  • Information Systems
  • Lessons Learned
  • Machine Learning
  • Natural Language Processing
  • Natural Language Understanding
  • Natural Languages
  • Neural Networks
  • Social Media
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Defense Technology Research and Development.
  • Organizational Process Management (OPM).
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Translation