Semi-supervised User Geolocation via Graph Convolutional Networks
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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-1187.pdf