Answering Count Questions with Structured Answers from Text

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Shrestha Ghosh - , Max Planck Institute for Informatics, Saarland University (Author)
  • Simon Razniewski - , Max Planck Institute for Informatics (Author)
  • Gerhard Weikum - , Max Planck Institute for Informatics (Author)

Abstract

In this work we address the challenging case of answering count queries in web search, such as number of songs by John Lennon. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries, including existing benchmark show the benefits of our method, and the influence of specific parameter settings. Our code, data and an interactive system demonstration are publicly available at https://github.com/ghoshs/CoQEx and https://nlcounqer.mpi-inf.mpg.de/.

Details

Original languageEnglish
Article number100769
JournalJournal of Web Semantics
Volume76
Publication statusPublished - Apr 2023
Peer-reviewedYes
Externally publishedYes

External IDs

ORCID /0000-0002-5410-218X/work/185318144

Keywords

Keywords

  • Count queries, Explainable AI, Question answering