Answering Count Questions with Structured Answers from Text

TL;DR

This paper introduces a novel approach for answering count questions in web search by inferring answers from multiple observations, supporting qualifiers, and providing evidence through instance enumeration, improving over prior methods.

Contribution

It presents a new methodology that infers count answers from multiple observations, supports semantic qualifiers, and offers evidence via representative instances, advancing count query answering.

Findings

Improved accuracy in count query answering.

Effective handling of semantic qualifiers.

Demonstrated benefits on benchmark datasets.

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/.