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

arXiv: https://arxiv.org/abs/2307.00509 · HTML: https://mlsanity.com/paper/2307.00509/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments](https://mlsanity.com/paper/1811.12354/) (2018)
- [Mapping Instructions and Visual Observations to Actions with Reinforcement Learning](https://mlsanity.com/paper/1704.08795/) (2017)
- [Talk the Walk: Navigating New York City through Grounded Dialogue](https://mlsanity.com/paper/1807.03367/) (2018)
- [Abstract Visual Reasoning with Tangram Shapes](https://mlsanity.com/paper/2211.16492/) (2022)
- [From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?](https://mlsanity.com/paper/2005.01330/) (2020)
- [ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization](https://mlsanity.com/paper/2010.03276/) (2020)
- [What's Wrong with Hebrew NLP? And How to Make it Right](https://mlsanity.com/paper/1908.05453/) (2019)
- [RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation](https://mlsanity.com/paper/1909.08970/) (2019)

## Built on by (most cited first, 3 of 3)

- [HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://mlsanity.com/paper/2406.03897/) (2024)
- [Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions](https://mlsanity.com/paper/2402.16364/) (2024)
- [Into the Unknown: Generating Geospatial Descriptions for New Environments](https://mlsanity.com/paper/2406.19967/) (2024)

## 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.