# Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

arXiv: https://arxiv.org/abs/1708.04358 · HTML: https://mlsanity.com/paper/1708.04358/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [From Languages to Geographies: Towards Evaluating Cultural Bias in Hate Speech Datasets](https://mlsanity.com/paper/2404.17874/) (2024)
- [Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution](https://mlsanity.com/paper/2305.11315/) (2023)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)
- [On the Universality of Deep Contextual Language Models](https://mlsanity.com/paper/2109.07140/) (2021)
- [Where on Earth Do Users Say They Are?: Geo-Entity Linking for Noisy Multilingual User Input](https://mlsanity.com/paper/2404.18784/) (2024)
- [Speech Analysis of Language Varieties in Italy](https://mlsanity.com/paper/2406.15862/) (2024)
- [Predicting the Geolocation of Tweets Using transformer models on Customized Data](https://mlsanity.com/paper/2303.07865/) (2023)
- [FewUser: Few-Shot Social User Geolocation via Contrastive Learning](https://mlsanity.com/paper/2404.08662/) (2024)

## Abstract (arXiv)

We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.