skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


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
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
More Like this
  1. ; ; ; ; (, Winter Conference on Applications of Computer Vision (WACV), 2021.)
    null (Ed.)
    We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classification methods demand a large amount of labeled data to attain good performance. However, annotation of a large dataset is expensive and time consuming. To minimize the dependence on a large annotated dataset, our proposed semi-supervised method trains from a small number of labeled examples and exploits two regulatory signals from unlabeled data. The first signal is the pseudo-labels of unlabeled examples computed from the confidences of the CNN being trained. The other is the normalized probabilities, as predicted by an image classifier CNN, that captures the information about appearances of the interesting objects in the video. We show that, under the supervision of these guiding signals from unlabeled examples, a video classification CNN can achieve impressive performances utilizing a small fraction of annotated examples on three publicly available datasets: UCF101, HMDB51, and Kinetics. 
    more » « less
  2. (, The 29th International Conference on Computational Linguistics (COLING 2022))
    During natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational awareness in nearly real time, and enable them to quickly distribute resources where most needed. However, annotating data for this purpose can be burdensome, subjective and expensive. In this paper, we investigate how to leverage the copious amounts of unlabeled data generated on social media by disaster eyewitnesses and affected individuals during disaster events. To this end, we propose a semi-supervised learning approach to improve the performance of neural models on several multimodal disaster tweet classification tasks. Our approach shows significant improvements, obtaining up to 7.7% improvements in F-1 in low-data regimes and 1.9% when using the entire training data. We make our code and data publicly available at https://github.com/iustinsirbu13/multimodal-ssl-for-disaster-tweet-classification. 
    more » « less
  3. ; (, European Conference on Machine Learning and Principles and Practice of Knowledge)
    Knowledge tracing (KT), or modeling student knowledge state given their past activity sequence, is one of the essential tasks in online education systems. Research has demonstrated that students benefit from both assessed (e.g., solving problems, which can be graded) and non-assessed learning activities (e.g., watching video lectures, which cannot be graded), and thus, modeling student knowledge from multiple types of activities with knowledge transfer between them is crucial. However, current approaches to multi-activity knowledge tracing cannot capture coarse-grained between-type associations and are primarily evaluated by predicting student performance on upcoming assessed activities (labeled data). Therefore, they are inadequate in incorporating signals from non-assessed activities (unlabeled data). We propose Graph-enhanced Multi-activity Knowledge Tracing (GMKT) that addresses these challenges by jointly learning a fine-grained recurrent memory-augmented student knowledge model and a coarse-grained graph neural network. In GMKT, we formulate multi-activity knowledge tracing as a semi-supervised sequence learning problem and optimize for accurate student performance and activity type at each time step. We demonstrate the effectiveness of our proposed model by experimenting on three real-world datasets. 
    more » « less
  4. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Modern machine learning algorithms typically require large amounts of labeled training data to fit a reliable model. To minimize the cost of data collection, researchers often employ techniques such as crowdsourcing and web scraping. However, web data and human annotations are known to exhibit high margins of error, resulting in sizable amounts of incorrect labels. Poorly labeled training data can cause models to overfit to the noise distribution, crippling performance in real-world applications. In this work, we investigate the viability of using data augmentation in conjunction with semi-supervised learning to improve the label noise robustness of image classification models. We conduct several experiments using noisy variants of the CIFAR-10 image classification dataset to benchmark our method against existing algorithms. Experimental results show that our augmentative SSL approach improves upon the state-of-the-art. 
    more » « less
  5. ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Modern machine learning algorithms typically require large amounts of labeled training data to fit a reliable model. To minimize the cost of data collection, researchers often employ techniques such as crowdsourcing and web scraping. However, web data and human annotations are known to exhibit high margins of error, resulting in sizable amounts of incorrect labels. Poorly labeled training data can cause models to overfit to the noise distribution, crippling performance in real-world applications. In this work, we investigate the viability of using data augmentation in conjunction with semi-supervised learning to improve the label noise robustness of image classification models. We conduct several experiments using noisy variants of the CIFAR-10 image classification dataset to benchmark our method against existing algorithms. Experimental results show that our augmentative SSL approach improves upon the state-of-the-art. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email