Testing the cluster hypothesis with focused and graded relevance judgments

Eilon Sheetrit, Anna Shtok, Oren Kurland, Igal Shprincis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The cluster hypothesis is a fundamental concept in ad hoc retrieval. Heretofore, cluster hypothesis tests were applied to documents using binary relevance judgments. We present novel tests that utilize graded and focused relevance judgments; the latter are markups of relevant text in relevant documents. Empirical exploration reveals that the cluster hypothesis holds not only for documents, but also for passages, as measured by the proposed tests. Furthermore, the hypothesis holds to a higher extent for highly relevant documents and for those that contain a high fraction of relevant text.

Original languageEnglish
Title of host publication41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
Pages1173-1176
Number of pages4
ISBN (Electronic)9781450356572
DOIs
StatePublished - 27 Jun 2018
Event41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018 - Ann Arbor, United States
Duration: 8 Jul 201812 Jul 2018

Publication series

Name41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018

Conference

Conference41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
Country/TerritoryUnited States
CityAnn Arbor
Period8/07/1812/07/18

Keywords

  • Cluster hypothesis
  • Inter-passage similarities
  • Passages

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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