The Best of Both Worlds: Combining Ranked List and Clustering,

Abstract

An information retrieval system arranges retrieved documents into a list and ranks them in the order they are expected to be relevant. The ranked list is a well-known and widely accepted technique for information presentation for the task of finding relevant documents as quick as possible. Clustering is also a well-known and successful method for grouping similar documents together - in particular, for grouping relevant documents. In this paper we present a system that combines the ranked list with a clustering approach, accepts a modest amount of user feedback, and yields an approach that exceeds the retrieval effectiveness of a traditional ranked list, with and without relevance feedback.

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Document Details

Document Type
Technical Report
Publication Date
May 03, 1999
Accession Number
ADA365608

Entities

People

  • Anton Leuski
  • James F. Allen

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Automatic
  • Clustering
  • Computer Programs
  • Computer Science
  • Embedding
  • Feedback
  • Information Retrieval
  • Judgment
  • Linear Programming
  • Materials
  • Models
  • Precision
  • Three Dimensional
  • Two Dimensional
  • Vector Spaces
  • Visualizations

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Instructional Design and Training Evaluation.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval