Transformer Based Geocoding

• URL: http://arxiv.org/abs/2301.01170v1
• Date: Mon, 2 Jan 2023 10:13:32 GMT
• Title: Transformer Based Geocoding
• Authors: Yuval Solaz and Vitaly Shalumov
• Abstract summary: We formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. We obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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