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

08/14/2017
by   Afshin Rahimi, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset