Automated Team Discourse Annotation and Performance Prediction Using LSA

Abstract

We describe two approaches to analyzing and tagging team discourse using Latent Semantic Analysis (LSA) to predict team performance. The first approach automatically categorizes the contents of each statement made by each of the three team members using an established set of tags. Performance predicting the tags automatically was 15% below human agreement. These tagged statements are then used to predict team performance. The second approach measures the semantic content of the dialogue of the team as a whole and accurately predicts the team's performance on a simulated military mission.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA460701

Entities

People

  • Melanie J. Martin
  • Peter W. Foltz

Organizations

  • New Mexico State University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Agreements
  • Algorithms
  • Automatic
  • Cognitive Systems Engineering
  • Computer Science
  • Flight Simulators
  • Frequency
  • Information Operations
  • Information Retrieval
  • Military Operations
  • Military Research
  • Natural Language Processing
  • New Mexico
  • Psychology
  • Robotics
  • Training
  • Uncertainty

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Regression Analysis.