Resource-Bounded Information Gathering for Correlation Clustering

Abstract

We present a new class of problems, called resource-bounded information gathering for correlation clustering. Our goal is to perform correlation clustering under circumstances in which accuracy may be improved by augmenting the given graph with additional information. This information is obtained by querying an external source under resource constraints. The problem is to develop the most effective query selection strategy to minimize some loss function on the resulting partitioning. We motivate the problem using an entity resolution task.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA464769

Entities

People

  • Andrew McCallum
  • Pallika Kanani

Organizations

  • University of Massachusetts Amherst

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Boundaries
  • Clustering
  • Computations
  • Contracts
  • Information Operations
  • Instructions
  • Learning
  • Machine Learning
  • Massachusetts
  • National Security
  • Security
  • Standards
  • Universities

Fields of Study

  • Computer science

Readers

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  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms