POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search

Nicola Maritan; Alessandro Moschitti; Federico Borazio; Xiaokun Zhou; Zhengwei Bai

POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search

Nicola Maritan, Alessandro Moschitti, Federico Borazio, Xiaokun Zhou, Zhengwei Bai

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Abstract

Points-of-Interest (POI) search, the task of retrieving and ranking geographically situated entities in response to a query issued from a specific location, underpins major mapping and navigation services. We present POI Search Suite (POISS), the first large-scale dataset designed for this task, pairing over 300K geo-anchored natural language queries across 7 languages and 14 locales with more than 4M graded relevance labels across over 90M candidate POIs drawn from the 72M-entity Overture Maps Places index, jointly capturing semantic match, geographic proximity, and entity quality. Our experiments assess the utility of the proposed dataset for advancing research on POI search.

Anthology ID:: 2026.emnlp-main.2497
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 56705–56723
Language:
URL:: https://aclanthology.org/2026.emnlp-main.2497/
DOI:
Bibkey:
Cite (ACL):: Nicola Maritan, Alessandro Moschitti, Federico Borazio, Xiaokun Zhou, and Zhengwei Bai. 2026. POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56705–56723, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search (Maritan et al., EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-main.2497.pdf

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@inproceedings{maritan-etal-2026-poiss,
    title = "{POISS}: A Large-Scale Multilingual Dataset for Point-of-Interest Search",
    author = "Maritan, Nicola  and
      Moschitti, Alessandro  and
      Borazio, Federico  and
      Zhou, Xiaokun  and
      Bai, Zhengwei",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2497/",
    pages = "56705--56723",
    ISBN = "979-8-89176-459-0",
    abstract = "Points-of-Interest (POI) search, the task of retrieving and ranking geographically situated entities in response to a query issued from a specific location, underpins major mapping and navigation services. We present POI Search Suite (POISS), the first large-scale dataset designed for this task, pairing over 300K geo-anchored natural language queries across 7 languages and 14 locales with more than 4M graded relevance labels across over 90M candidate POIs drawn from the 72M-entity Overture Maps Places index, jointly capturing semantic match, geographic proximity, and entity quality. Our experiments assess the utility of the proposed dataset for advancing research on POI search."
}

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%0 Conference Proceedings
%T POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search
%A Maritan, Nicola
%A Moschitti, Alessandro
%A Borazio, Federico
%A Zhou, Xiaokun
%A Bai, Zhengwei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F maritan-etal-2026-poiss
%X Points-of-Interest (POI) search, the task of retrieving and ranking geographically situated entities in response to a query issued from a specific location, underpins major mapping and navigation services. We present POI Search Suite (POISS), the first large-scale dataset designed for this task, pairing over 300K geo-anchored natural language queries across 7 languages and 14 locales with more than 4M graded relevance labels across over 90M candidate POIs drawn from the 72M-entity Overture Maps Places index, jointly capturing semantic match, geographic proximity, and entity quality. Our experiments assess the utility of the proposed dataset for advancing research on POI search.
%U https://aclanthology.org/2026.emnlp-main.2497/
%P 56705-56723

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Markdown (Informal)

[POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search](https://aclanthology.org/2026.emnlp-main.2497/) (Maritan et al., EMNLP 2026)

POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search (Maritan et al., EMNLP 2026)

ACL

Nicola Maritan, Alessandro Moschitti, Federico Borazio, Xiaokun Zhou, and Zhengwei Bai. 2026. POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56705–56723, Budapest, Hungary. Association for Computational Linguistics.