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Author  |
Hongmin Li; Doina Caragea; Cornelia Caragea |
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Title |
Combining Self-training with Deep Learning for Disaster Tweet Classification |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Volume |
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Issue |
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Pages |
719-730 |
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Keywords |
Domain Adaptation, Self-training, Crisis Tweets Classification, BERT, CNN |
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Abstract |
Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets. |
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Address |
Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University; Department of Computer Science, University of Illinois at Chicago |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Edition |
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ISSN |
978-1-949373-61-5 |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
hongminli@ksu.edu |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2367 |
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