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A Neural Model for User Geolocation and Lexical Dialectology

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arxiv 1704.04008 v3 pith:X3RLOTO5 submitted 2017-04-13 cs.CL

Afshin Rahimi , Trevor Cohn , Timothy Baldwin This is my paper · ORCID
classification cs.CL
keywords geolocationdialectalhiddenlayermodelneuraltermsuser
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We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by...

  2. Towards Interactive Global Geolocation Assistant

    cs.CV 2024-12 conditional novelty 6.0 of 10

    GaGA is an MLLM-based interactive geolocation system that improves country-level accuracy by 4.57% and city-level accuracy by 2.92% over OSV-5M-Baseline on a reproduced GWS15k benchmark.

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