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Title: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.  more » « less
Award ID(s):
1741345
NSF-PAR ID:
10472647
Author(s) / Creator(s):
; ; ;
Editor(s):
Radianti, Jaziar; Dokas, Ioannis; Lalone, Nicolas; Khazanchi, Deepak
Publisher / Repository:
ISCRAM 2023
Date Published:
Journal Name:
Proceedings of the 20th International ISCRAM Conference
Page Range / eLocation ID:
385-395
Subject(s) / Keyword(s):
["Crisis Tweet Classification","Semi-Supervised Few-Shot Learning","Pseudo-Labeling","TextMixUp."]
Format(s):
Medium: X
Location:
Omaha, USA
Sponsoring Org:
National Science Foundation
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