  Microblogging platforms such as Twitter provide active communication channels
during mass convergence and emergency events such as earthquakes, typhoons.
During the sudden onset of a crisis situation, affected people post useful
information on Twitter that can be used for situational awareness and other
humanitarian disaster response efforts, if processed timely and effectively.
Processing social media information pose multiple challenges such as parsing
noisy, brief and informal messages, learning information categories from the
incoming stream of messages and classifying them into different classes among
others. One of the basic necessities of many of these tasks is the availability
of data, in particular human-annotated data. In this paper, we present
human-annotated Twitter corpora collected during 19 different crises that took
place between 2013 and 2015. To demonstrate the utility of the annotations, we
train machine learning classifiers. Moreover, we publish first largest word2vec
word embeddings trained on 52 million crisis-related tweets. To deal with
tweets language issues, we present human-annotated normalized lexical resources
for different lexical variations.
