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arXiv 1804.080492018-04cs.CL

Semi-supervised User Geolocation via Graph Convolutional Networks

The University of Melbourne（墨尔本大学）

Afshin Rahimi, Trevor Cohn, Timothy Baldwin

备注：: ACL2018

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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.

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