Passage Retrieval and Evaluation

Abstract

Information retrieval researchers have studied passage retrieval extensively, yet there is no consensus within the community about how to evaluate the results of passage retrieval experiments. This paper describes five character-level passage evaluation measures and tasks for which they may be appropriate. In the second half of the paper we compare several passage retrieval models, including a new generative mixture model that outperforms strong baselines on many of the evaluation measures discussed in part one.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2005
Accession Number
ADA478017

Entities

People

  • Courtney Wade
  • James Allan

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Computer Science
  • Equations
  • Information Retrieval
  • Information Science
  • Judgment
  • Kernel Functions
  • Language
  • Machine Learning
  • Personality
  • Precision
  • Probability
  • Probability Distributions
  • Supervised Machine Learning
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval