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

Afshin Rahimi; Timothy Baldwin; Trevor Cohn

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arXiv · 1708.04358

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

Afshin Rahimi·Timothy Baldwin·Trevor Cohn

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Abstract

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.

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Afshin Rahimi, Timothy Baldwin, Trevor Cohn. 2017-08-14. Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks. https://arxiv.org/abs/1708.04358

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