Utilizing focused relevance feedback

Elinor Brondwine, Anna Shtok, Oren Kurland

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

Abstract

We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by using relevant and non-relevant information separately. Empirical exploration attests to the merits of our methods, and sheds light on the effectiveness of using and integrating relevance feedback for textual units of varying granularities.

Original languageEnglish
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1061-1064
Number of pages4
ISBN (Electronic)9781450342902
DOIs
StatePublished - 7 Jul 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Publication series

NameSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
Country/TerritoryItaly
CityPisa
Period17/07/1621/07/16

Keywords

  • Focused relevance feedback

ASJC Scopus subject areas

  • Information Systems
  • Software

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