Answering Count Questions with Structured Answers from Text
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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 language | English |
|---|---|
| Article number | 100769 |
| Journal | Journal of Web Semantics |
| Volume | 76 |
| Publication status | Published - Apr 2023 |
| Peer-reviewed | Yes |
| Externally published | Yes |
External IDs
| ORCID | /0000-0002-5410-218X/work/185318144 |
|---|
Keywords
ASJC Scopus subject areas
Keywords
- Count queries, Explainable AI, Question answering