K-Means Subject Matter Expert Refined Topic Model Methodology

Abstract

We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Expert Refined Topic methodology using the Visual Basic for Applications programming language. This topic modeling software is deployable across the majority of the Department of Defense computing environments and allows analysts to develop topic models using a graphical user interface.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2017
Accession Number
AD1028777

Entities

People

  • Nathan Parker
  • Theodore T. Allen
  • Zhenhuan Sui

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Basic Programming Language
  • Cognitive Science
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Data Mining
  • Department Of Defense
  • Information Processing
  • Information Retrieval
  • Information Science
  • Language
  • Machine Learning
  • Monte Carlo Method
  • Programming Languages
  • Sampling
  • Software Development
  • User Interface

Fields of Study

  • Computer science

Readers

  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.