Títol:
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Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages
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Autor/a:
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Imran, Muhammad; Mitra, Prasenjit; Castillo, Carlos
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Abstract:
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Comunicació presentada a: LREC 2016, Tenth International Conference on Language Resources and Evaluation, celebrada del 23 al 28 de maig de 2016 a Portorož, Eslovènia. |
Abstract:
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Abstract:
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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. |
Matèries:
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-Natural language processing -Twitter -Disaster response -Supervised classification |
Drets:
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© The European Language Resources Association. The LREC 2016 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
https://creativecommons.org/licenses/by-nc/4.0/
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Tipus de document:
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Objecte de conferència Article - Versió publicada |
Publicat per:
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LREC
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