Answering Count Questions with Structured Answers from Text

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Shrestha Ghosh - , Max-Planck-Institut für Informatik, Universität des Saarlandes (Autor:in)
  • Simon Razniewski - , Max-Planck-Institut für Informatik (Autor:in)
  • Gerhard Weikum - , Max-Planck-Institut für Informatik (Autor:in)

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

OriginalspracheEnglisch
Aufsatznummer100769
FachzeitschriftJournal of Web Semantics
Jahrgang76
PublikationsstatusVeröffentlicht - Apr. 2023
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

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

Schlagworte

Schlagwörter

  • Count queries, Explainable AI, Question answering