In this paper, we formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. Using this formulation, we obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output. The geocoding model was trained on geo-tagged wikidump data with adaptive cell partitioning for the geolocation representation. All of the code including Rest-based application, dataset and model checkpoints used in this work are publicly available.
Cite
@article{arxiv.2301.01170,
title = {Transformer Based Geocoding},
author = {Yuval Solaz and Vitaly Shalumov},
journal= {arXiv preprint arXiv:2301.01170},
year = {2023}
}