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

EMNLP 2017 Afshin RahimiTimothy BaldwinTrevor Cohn

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... (read more)

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