@inproceedings{kulkarni-etal-2021-multi,
title = "Multi-Level Gazetteer-Free Geocoding",
author = "Kulkarni, Sayali and
Jain, Shailee and
Hosseini, Mohammad Javad and
Baldridge, Jason and
Ie, Eugene and
Zhang, Li",
editor = "Alikhani, Malihe and
Blukis, Valts and
Kordjamshidi, Parisa and
Padmakumar, Aishwarya and
Tan, Hao",
booktitle = "Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.splurobonlp-1.9",
doi = "10.18653/v1/2021.splurobonlp-1.9",
pages = "79--88",
abstract = "We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth{'}s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates{---}and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.",
}
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%0 Conference Proceedings
%T Multi-Level Gazetteer-Free Geocoding
%A Kulkarni, Sayali
%A Jain, Shailee
%A Hosseini, Mohammad Javad
%A Baldridge, Jason
%A Ie, Eugene
%A Zhang, Li
%Y Alikhani, Malihe
%Y Blukis, Valts
%Y Kordjamshidi, Parisa
%Y Padmakumar, Aishwarya
%Y Tan, Hao
%S Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kulkarni-etal-2021-multi
%X We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth’s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates—and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.
%R 10.18653/v1/2021.splurobonlp-1.9
%U https://aclanthology.org/2021.splurobonlp-1.9
%U https://doi.org/10.18653/v1/2021.splurobonlp-1.9
%P 79-88
Markdown (Informal)
[Multi-Level Gazetteer-Free Geocoding](https://aclanthology.org/2021.splurobonlp-1.9) (Kulkarni et al., splurobonlp 2021)
ACL
- Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, and Li Zhang. 2021. Multi-Level Gazetteer-Free Geocoding. In Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics, pages 79–88, Online. Association for Computational Linguistics.