Investigating Semi-Supervised Learning Algorithms in Text Datasets
2022 Innovations in Intelligent Systems and Applications Conference (ASYU)(2024)
Abstract
Using large training datasets enhances the generalization capabilities of
neural networks. Semi-supervised learning (SSL) is useful when there are few
labeled data and a lot of unlabeled data. SSL methods that use data
augmentation are most successful for image datasets. In contrast, texts do not
have consistent augmentation methods as images. Consequently, methods that use
augmentation are not as effective in text data as they are in image data. In
this study, we compared SSL algorithms that do not require augmentation; these
are self-training, co-training, tri-training, and tri-training with
disagreement. In the experiments, we used 4 different text datasets for
different tasks. We examined the algorithms from a variety of perspectives by
asking experiment questions and suggested several improvements. Among the
algorithms, tri-training with disagreement showed the closest performance to
the Oracle; however, performance gap shows that new semi-supervised algorithms
or improvements in existing methods are needed.
MoreTranslated text
Key words
semi supervised learning,self-training,co-training,tri-training,tri-training with disagreement
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