Classifying text messages for the haiti earthquake

By Cornelia Caragea, Nathan J. Mcneese, Anuj R. Jaiswal, Gregory W. Traylor, Hyun woo Kim, Prasenjit Mitra, Dinghao Wu, Andrea H. Tapia, Lee Giles, Bernard Jim Jansen and 1 more. 2011

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Universal Language Model Fine-tuning for Text Classification (2018, background): “, 2012), emergency response (Caragea et al., 2011), and commercial document classification, such as for legal discovery (Roitblat et al.”
Fine-tuned Language Models for Text Classification (2018, background): “…(NLP) tasks with many important real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classiﬁcation, such as for legal discovery (Roitblat et al., 2010).”
Processing Social Media Messages in Mass Emergency (2014, methodology): “agea et al. 2011]; built environment [Vieweg et al. 2010]; closure and services [Hughes et al. 2014b] Needs and donations donation of money, goods, services [Imran et al. 2013b]; food/water shortage [Caragea et al. 2011]; donations or volunteering [Olteanu et al. 2014]; help requests, relief coordin”