Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
Abstract
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.
Keywords
Cite
@article{arxiv.2105.09392,
title = {Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions},
author = {Gengchen Mai and Krzysztof Janowicz and Rui Zhu and Ling Cai and Ni Lao},
journal= {arXiv preprint arXiv:2105.09392},
year = {2021}
}
Comments
20 pages, 3 figure, Full paper accepted to AGILE 2021