Accession Number:

ADA478104

Title:

Learning Threshold Parameters for Event Classification in Broadcast News

Descriptive Note:

Technical rept.

Corporate Author:

MASSACHUSETTS UNIV AMHERST CENTER FOR INTELLIGENT INFORMATION RETRIEVAL

Personal Author(s):

• Papka, Ron

Report Date:

1999-01-01

Pagination or Media Count:

8.0

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.

Descriptors:

• *ALGORITHMS
• *DATA PROCESSING
• *INFORMATION RETRIEVAL
• *TRACKING
• PARAMETERS
• LEARNING
• TEXT PROCESSING
• CLASSIFICATION
• THRESHOLD EFFECTS
• ACCURACY
• ESTIMATES

Subject Categories:

• Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE