Exploiting Topic Pragmatics for New Event Detection in TDT-2003

Abstract

Stottler Henke participated for the first time in the New Event Detection (NED) track of TDT-2003 as a means of evaluating various prototyped components developed as part of a new story detection and topic tracking application. We combined a number of "pragmatics-based" classifiers in an ensemble-learning framework to identify the first story of a new topic to link subsequent stories together as they unfold across multiple news streams. We present an overview of our techniques and a preliminary characterization of their performances based on our experimental runs for the TDT-2003 Evaluation.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA439331

Entities

People

  • Ronald K. Braun
  • Ryan Kaneshiro

Organizations

  • Stottler Henke Associates

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Bayesian Networks
  • Best Practices
  • Data Science
  • Data Sets
  • Detection
  • Errors
  • Event Detection
  • False Alarms
  • Language
  • Machine Learning
  • Models
  • Probability
  • Semantic Models
  • Test And Evaluation
  • Warning Systems

Readers

  • Military History
  • Sensor Fusion and Tracking Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.