Contract Information Extraction Using Machine Learning

Abstract

The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory's high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21 percent of the contracts have their information correctly extracted with this process. To provide an accurate depiction of availability for every part, an improvement is needed. The likely most effective improvement is to develop a custom NER model.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1146124

Entities

People

  • Zachary E. Butcher

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Languages
  • Computers
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Information Science
  • Kernel Functions
  • Law
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Natural Languages
  • Ontologies
  • Parallel Computing
  • Recurrent Neural Networks
  • Supervised Machine Learning

Readers

  • Computational Linguistics
  • Government Contracting/Procurement.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks