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Author (up) Muhammad Imran; Prasenjit Mitra; Jaideep Srivastava pdf  isbn
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  Title Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages Type Conference Article
  Year 2016 Publication ISCRAM 2016 Conference Proceedings 13th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2016  
  Volume Issue Pages  
  Keywords Social Media; Tweets Classification; Domain Adaptation  
  Abstract 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.  
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  Corporate Author Thesis  
  Publisher Federal University of Rio de Janeiro Place of Publication Rio de Janeiro, Brasil Editor A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3388 ISBN 978-84-608-7984-9 Medium  
  Track Social Media Studies Expedition Conference 13th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 1396  
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