Ranking documents by answer-passage quality

Evi Yulianti, Ruey Cheng Chen, Falk Scholer, W. Bruce Croft, Mark Sanderson

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

7 Citations (Scopus)

Abstract

Evidence derived from passages that closely represent likely answers to a posed query can be useful input to the ranking process. Based on a novel use of Community Question Answering data, we present an approach for the creation of such passages. A general framework for extracting answer passages and estimating their quality is proposed, and this evidence is integrated into ranking models. Our experiments on two web collections show that such quality estimates from answer passages provide a strong indication of document relevance and compare favorably to previous passage-based methods. Combining such evidence can significantly improve over a set of state-of-the-art ranking models, including Quality-Biased Ranking, External Expansion, and a combination of both. A final ranking model that incorporates all quality estimates achieves further improvements on both collections.

Original languageEnglish
Title of host publication41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018
PublisherAssociation for Computing Machinery, Inc
Pages335-344
Number of pages10
ISBN (Electronic)9781450356572
DOIs
Publication statusPublished - 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

  • Answer passages
  • Document ranking
  • Quality estimation

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