Statistical QA - Classifier vs. Re-Ranker: What's the Difference

Abstract

In this paper, we show that we can obtain a good baseline performance for Question Answering (QA) by using only 4 simple features. Using these features, we contrast two approaches used for a Maximum Entropy based QA system. We view the QA problem as a classification problem and as a reranking problem. Our results indicate that the QA system viewed as a reranker clearly outperforms the QA system used as a classifier. Both systems are trained using the same data.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA460400

Entities

People

  • Deepak Ravichandran
  • Eduard Hovy
  • Franz J. Och

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Applied Computer Science
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • California
  • Classification
  • Computational Processes
  • Data Science
  • Data Sets
  • Information Retrieval
  • Information Science
  • Machine Learning
  • Natural Languages
  • Precision
  • Probability
  • Test And Evaluation
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Neural Network Machine Learning.