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Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

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arxiv 2105.09392 v1 pith:DYCG4INL submitted 2021-05-19 cs.CL cs.AI

classification cs.CLcs.AI
keywords geographicquestionsansweringquestiongeoqaanswerchallengesclassification
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A unified geo-benchmark of 421k questions across knowledge, reasoning, and application tasks, showing that thinking mode can help small models close the gap with much larger ones.

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