Semi-supervised User Geolocation via Graph Convolutional Networks

Afshin Rahimi, Trevor Cohn, Timothy Baldwin

[How to correct problems with metadata yourself]


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.
Anthology ID:
P18-1187
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2009–2019
Language:
URL:
https://aclanthology.org/P18-1187
DOI:
10.18653/v1/P18-1187
Bibkey:
Cite (ACL):
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised User Geolocation via Graph Convolutional Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2009–2019, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Semi-supervised User Geolocation via Graph Convolutional Networks (Rahimi et al., ACL 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P18-1187.pdf
Note:
 P18-1187.Notes.pdf
Presentation:
 P18-1187.Presentation.pdf
Video:
 https://preview.aclanthology.org/teach-a-man-to-fish/P18-1187.mp4