@inproceedings{rahimi-etal-2017-continuous,
    title = "Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks",
    author = "Rahimi, Afshin  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-1016/",
    doi = "10.18653/v1/D17-1016",
    pages = "167--176",
    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."
}