mlsanityData to 13 Oct 2026

HeGeL: A Novel Dataset for Geo-Location from Hebrew Text

arXiv 2307.00509 ↗First posted 2 Jul 2023cs.CL cs.AI cs.IRcited by 3

Not on the map. Why?

Abstract (arXiv)

The task of textual geolocation - retrieving the coordinates of a place based on a free-form language description - calls for not only grounding but also natural language understanding and geospatial reasoning. Even though there are quite a few datasets in English used for geolocation, they are currently based on open-source data (Wikipedia and Twitter), where the location of the described place is mostly implicit, such that the location retrieval resolution is limited. Furthermore, there are no datasets available for addressing the problem of textual geolocation in morphologically rich and resource-poor languages, such as Hebrew. In this paper, we present the Hebrew Geo-Location (HeGeL) corpus, designed to collect literal place descriptions and analyze lingual geospatial reasoning. We crowdsourced 5,649 literal Hebrew place descriptions of various place types in three cities in Israel. Qualitative and empirical analysis show that the data exhibits abundant use of geospatial reasoning and requires a novel environmental representation.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 2 Jul 2023.

Builds on

13 of 39 references.

References · 13

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1 more
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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrewcited by 6
  2. 2024Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptionscited by 3
  3. 2024Into the Unknown: Generating Geospatial Descriptions for New Environmentscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Quantifying Geospatial in the Common Crawl Corpus
  2. 2024GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding
  3. 2023GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
  4. 2025Coordinates from Context: Using LLMs to Ground Complex Location References
  5. 2023Are Large Language Models Geospatially Knowledgeable?
  6. 2025Geospatial Mechanistic Interpretability of Large Language Models
  7. 2026PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment
  8. 2026Much of Geospatial Web Search Is Beyond Traditional GIS