Learning Threshold Parameters for Event Classification in Broadcast News

Abstract

In this paper we present two methods for automatic threshold parameter estimation for an event tracking algorithm. We view the threshold as a statistic of the incoming data stream, which is assumed to contain broadcast news stories from radio, television, and newswire sources. Query bias defined in terms of threshold estimators can be identified when a word co-occurrence representation for text is used. Our results suggest that both approaches learn bias from training corpora, leading to improved classification accuracy for event tracking applications.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1999
Accession Number
ADA478104

Entities

People

  • Ron Papka

Organizations

  • University of Massachusetts Amherst

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Automatic
  • Availability
  • Classification
  • Computers
  • Contracts
  • Estimators
  • Information Operations
  • Instructions
  • Learning
  • Massachusetts
  • Monitoring
  • Security
  • Speed Regulators
  • Standards

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computer Vision.
  • Statistical inference.