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Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages
Muhammad Imran
Prasenjit Mitra
Jaideep Srivastava
A. Tapia
P. Antunes
V.A. Bañuls
K. Moore
J. Porto
Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
urn:ISBN:978-84-608-7984-9
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Cross-Language%20Domain%20Adaptation%20for%20Classifying%20Crisis-Related%20Short%20Messages&stitle=ISCRAM%202016&issn=2411-3388&isbn=978-84-608-7984-9&date=2016&aulast=Muhammad%20Imran&au=Prasenjit%20Mitra&au=Jaideep%20Srivastava&pub=Federal%20University%20of%20Rio%20de%20Janeiro&place=Rio%20de%20Janeiro%2C%20Brasil&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=1396
citekey:MuhammadImran_etal2016
citation:Muhammad Imran, Prasenjit Mitra, & Jaideep Srivastava. (2016). Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
2016
ConferencePaper
text
Social Media
Tweets Classification
Domain Adaptation
file:http://idl.iscram.org/files/muhammadimran/2016/1396_MuhammadImran_etal2016.pdf
Federal University of Rio de Janeiro
English
2411-3388
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management
2016
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