A. Zielinski, S. E. Middleton, L. N. Tokarchuk, and X. Wang, Social media text mining and network analysis for decision support in natural crisis management, Proc. ISCRAM, 2013.

M. Imran, C. Castillo, F. Diaz, and S. Vieweg, Processing Social Media Messages in Mass Emergency, ACM Computing Surveys, vol.47, issue.4, 2015.
DOI : 10.1109/MIS.2012.6

T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes Twitter users, Proceedings of the 19th international conference on World wide web, WWW '10, 2010.
DOI : 10.1145/1772690.1772777

S. T. Dumais, Latent semantic analysis. Annual review of information science and technology, 2004.

R. Mihalcea and P. Tarau, Textrank: Bringing order into texts, Conference on Empirical Methods in Natural Language Processing, 2004.

G. Erkan and D. R. Radev, Lexrank: Graph-based lexical centrality as salience in text summarization, Journal of Artificial Intelligence Research, 2004.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, 2015.
DOI : 10.1007/s10994-013-5335-x

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke et al., A novel connectionist system for unconstrained handwriting recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.5, p.31, 2009.
DOI : 10.1109/tpami.2008.137

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Farahat and R. Halavati, Noise Robust Speech Recognition Using Deep Belief Networks, International Journal of Computational Intelligence and Applications, vol.35, issue.01, 2016.
DOI : 10.1007/978-3-540-89985-3_60

A. Karpathy, J. Johnson, and F. F. Li, Visualizing and understanding recurrent networks, 2015.

J. Yin, A. Lampert, M. Cameron, B. Robinson, and R. Power, Using Social Media to Enhance Emergency Situation Awareness, IEEE Intelligent Systems, vol.27, issue.6, 2012.
DOI : 10.1109/MIS.2012.6

M. Imran, C. Castillo, J. Lucas, P. Meier, and S. Vieweg, AIDR, Proceedings of the 23rd International Conference on World Wide Web, WWW '14 Companion, 2014.
DOI : 10.1002/spe.993

Z. Ashktorab, C. Brown, M. Nandi, and A. Culotta, Tweedr: Mining twitter to inform disaster response, Proc. of ISCRAM, 2014.

A. Musaev, D. Wang, and C. Pu, Litmus: Landslide detection by integrating multiple sources, 11th International Conference Information Systems for Crisis Response and Management (ISCRAM), 2014.

J. Rogstadius, M. Vukovic, C. Teixeira, V. Kostakos, E. Karapanos et al., CrisisTracker: Crowdsourced social media curation for disaster awareness, IBM Journal of Research and Development, vol.57, issue.5, 2013.
DOI : 10.1147/JRD.2013.2260692

M. Berlingerio, F. Calabrese, D. Lorenzo, G. Dong, X. Gkoufas et al., SaferCity: A System for Detecting and Analyzing Incidents from Social Media, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013.
DOI : 10.1109/ICDMW.2013.39

K. Kireyev, L. Palen, and K. Anderson, Applications of topics models to analysis of disaster-related twitter data, NIPS Workshop on Applications for Topic Models: Text and Beyond, p.Whistler, 2009.

A. Graves, Supervised Sequence Labelling, 2012.
DOI : 10.1007/978-3-642-24797-2_2

R. Pascanu, T. Mikolov, and Y. Bengio, On the difficulty of training recurrent neural networks. arXiv preprint arXiv:1211, p.5063, 2012.

A. Olteanu, C. Castillo, F. Diaz, and S. Vieweg, Crisislex: A lexicon for collecting and filtering microblogged communications in crises, In: ICWSM, 2014.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, 2014.

Y. A. Lecun, L. Bottou, G. B. Orr, K. R. Müller, and B. Fbi, Efficient backprop In: Neural networks: Tricks of the trade Updates on investigation into multiple explosions in boston, 2012.

C. Gillam and C. Maclaggan, Ammonium nitrate stores exploded at texas plant: state agency, 2013.