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- W2804079529 abstract "During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situ- ational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised tech- nique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real- world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data." @default.
- W2804079529 created "2018-06-01" @default.
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- W2804079529 date "2018-06-15" @default.
- W2804079529 modified "2023-10-17" @default.
- W2804079529 title "Graph Based Semi-Supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets" @default.
- W2804079529 doi "https://doi.org/10.1609/icwsm.v12i1.15047" @default.
- W2804079529 hasPublicationYear "2018" @default.
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