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

What it is about

The task is to turn a free-text place description into map coordinates. The authors crowdsourced 5,649 literal Hebrew place descriptions for 167 places in Tel Aviv, Haifa and Jerusalem. Writers described a place they know without naming it or any landmark within 100 m; a second participant then marked it on OpenStreetMap, and a description was kept only if the marked point lay within 300 m of the target. The texts lean on cardinal directions (about 40% of sampled descriptions), spatial prepositions and definite descriptions, so the authors argue that a whole-city representation is needed, not a route view. Baselines: a Google Maps query on the raw text reaches 27.7% task completion (TC), an oracle NER over all n-grams 37.8%, and a dual encoder (AlephBERT plus an OSM cell graph) only 2.4% when trained on other cities. Human agreement is 70.8%.

Ideas

For Mova

I1 goes into Prepositions, prefixes, conjunctions and particles (spatial prepositions) and into the relation types of world. I2 and I3 are measurements for 12. Mova and Modern Hebrew: where it stands, what it would need, the plan for a Modern Hebrew reader; this is a test set, not a parser feature.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.findings-acl.460

@inproceedings{pazargaman2023hegel,
  title = {{HeGeL: A Novel Dataset for Geo-Location from Hebrew Text}},
  author = {Paz-Argaman, Tzuf and Bauman, Tal and Mondshine, Itai and Omer, Itzhak and Dalyot, Sagi and Tsarfaty, Reut},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-acl.460},
  url = {https://doi.org/10.18653/v1/2023.findings-acl.460}
}

References

26 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Anne H Anderson, Miles Bader, Ellen Gurman Bard, Elizabeth Boyle, Gwyneth Doherty, Simon Garrod, Stephen Isard, Jacqueline Kowtko, Jan McAllister, Jim Miller, et al. 1991. The hcrc map task corpus. Language and speech, 34(4):351–366. find
  2. Jason Baldridge, Tania Bedrax-Weiss, Daphne Luong, Srini Narayanan, Bo Pang, Fernando Pereira, Radu Soricut, Michael Tseng, and Yuan Zhang. 2018. Points, paths, and playscapes: Large-scale spatial language understanding tasks set in the real world. In Proceedings of the First International Workshop on Spatial Language Understanding, pages 46–52. find
  3. Howard Chen, Alane Suhr, Dipendra Misra, Noah Snavely, and Yoav Artzi. 2019. Touchdown: Natural language navigation and spatial reasoning in visual street environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12538–12547. find
  4. Harm De Vries, Kurt Shuster, Dhruv Batra, Devi Parikh, Jason Weston, and Douwe Kiela. 2018. Talk the walk: Navigating new york city through grounded dialogue. arXiv preprint arXiv:1807.03367. ↗
  5. Jacob Eisenstein, Brendan O’Connor, Noah A Smith, and Eric Xing. 2010. A latent variable model for geographic lexical variation. In Proceedings of the 2010 conference on empirical methods in natural language processing, pages 1277–1287. find
  6. Daniel Fried, Nicholas Tomlin, Jennifer Hu, Roma Patel, and Aida Nematzadeh. 2022. Pragmatics in grounded language learning: Phenomena, tasks, and modeling approaches. arXiv preprint arXiv:2211.08371. ↗
  7. Bo Han, Paul Cook, and Timothy Baldwin. 2012. Geolocation prediction in social media data by finding location indicative words. In Proceedings of COL-ING 2012, pages 1045–1062. find
  8. William G Hayward and Michael J Tarr. 1995. Spatial language and spatial representation. Cognition, 55(1):39–84. find
  9. David Hilbert. 1935. Über die stetige abbildung einer linie auf ein flächenstück. In Dritter Band: Analysis· Grundlagen der Mathematik· Physik Verschiedenes, pages 1–2. Springer. find
  10. Anya Ji, Noriyuki Kojima, Noah Rush, Alane Suhr, Wai Keen Vong, Robert D Hawkins, and Yoav Artzi. 2022. Abstract visual reasoning with tangram shapes. arXiv preprint arXiv:2211.16492. ↗
  11. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. find
  12. Isabelle Kniestedt, Iulia Lefter, Stephan Lukosch, and Frances M Brazier. 2022. Re-framing engagement for applied games: A conceptual framework. Entertainment Computing, 41:100475. find
  13. Saul A Kripke. 1972. Naming and necessity. In Semantics of natural language, pages 253–355. Springer. find
  14. Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie, and Jason Baldridge. 2020. Room-across-room: Multilingual vision-and-language navigation with dense spatiotemporal grounding. arXiv preprint arXiv:2010.07954. ↗
  15. Kevin Lynch. 1960. The image of the environment. The image of the city, 11:1–13. find
  16. Matt MacMahon, Brian Stankiewicz, and Benjamin Kuipers. 2006. Walk the talk: Connecting language, knowledge, and action in route instructions. Def, 2(6):4. find
  17. Dipendra Misra, John Langford, and Yoav Artzi. 2017. Mapping instructions and visual observations to actions with reinforcement learning. arXiv preprint arXiv:1704.08795. ↗
  18. Amir More, Amit Seker, Victoria Basmova, and Reut Tsarfaty. 2019. Joint transition-based models for morpho-syntactic parsing: Parsing strategies for mrls and a case study from modern hebrew. Transactions of the Association for Computational Linguistics, 7:33–48. find our card
  19. Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik, and Reut Tsarfaty. 2020. Zest: Zero-shot learning from text descriptions using textual similarity and visual summarization. arXiv preprint arXiv:2010.03276. ↗
  20. Tzuf Paz-Argaman and Reut Tsarfaty. 2019. Run through the streets: A new dataset and baseline models for realistic urban navigation. arXiv preprint arXiv:1909.08970. ↗
  21. Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, and Anton van den Hengel. 2020. Reverie: Remote embodied visual referring expression in real indoor environments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9982–9991. find
  22. Amit Seker, Elron Bandel, Dan Bareket, Idan Brusilovsky, Refael Greenfeld, and Reut Tsarfaty. 2022. Alephbert: Language model pre-training and evaluation from sub-word to sentence level. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46–56. find our card
  23. Alexander W Siegel and Sheldon H White. 1975. The development of spatial representations of large-scale environments. Advances in child development and behavior, 10:9–55. find
  24. Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer. 2020. Vision-and-dialog navigation. In Conference on Robot Learning, pages 394–406. PMLR. find
  25. Reut Tsarfaty, Dan Bareket, Stav Klein, and Amit Seker. 2020. From spmrl to nmrl: What did we learn (and unlearn) in a decade of parsing morphologically-rich languages (mrls)? arXiv preprint arXiv:2005.01330. ↗
  26. Reut Tsarfaty, Amit Seker, Shoval Sadde, and Stav Klein. 2019. What’s wrong with hebrew nlp? and how to make it right. arXiv preprint arXiv:1908.05453. ↗ our card

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