Semi-supervised User Geolocation via Graph Convolutional Networks
Computation and Language
2018-05-16 v4
Abstract
Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our model achieves or is competitive with the state- of-the-art over three benchmark geolocation datasets when sufficient supervision is available. We also evaluate GCN under a minimal supervision scenario, and show it outperforms baselines. We find that highway network gates are essential for controlling the amount of useful neighbourhood expansion in GCN.
Cite
@article{arxiv.1804.08049,
title = {Semi-supervised User Geolocation via Graph Convolutional Networks},
author = {Afshin Rahimi and Trevor Cohn and Timothy Baldwin},
journal= {arXiv preprint arXiv:1804.08049},
year = {2018}
}
Comments
ACL2018