Semi-supervised User Geolocation via Graph Convolutional Networks

2018-04-22 · ACL 2018 7 · Afshin Rahimi, Trevor Cohn, Timothy Baldwin

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.

📄 PDF Abstract BibTeX arXiv:1804.08049

Code (1)

afshinrahimi/geographconv

Tasks

Event Detection

Methods 이 논문이 사용한 방법론

Graph Convolutional Networks 설명 없음

Sigmoid Activation 설명 없음

Highway Layer 설명 없음

Highway Network A Highway Network is an architecture designed to ease gradient-based training of very deep networks. They allow unimpeded information flow across several layers on…

GCN A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of [convolutional neural…

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