Context-Aware Contrastive Representation Learning for Zero-Shot Biomedical Text Classification.

Area of Science:

• Biomedical Informatics
• Natural Language Processing
• Machine Learning

Background:

• Biomedical text classification typically requires extensive human-annotated data, which is costly and time-consuming.
• Existing methods often struggle with data scarcity and high annotation expenses.

Purpose of the Study:

• To develop a zero-shot learning (ZSL) approach for biomedical text classification that eliminates the need for labeled training data.
• To leverage label surface names for training and inference, making the process more efficient.

Main Methods:

• Proposed a novel context-aware contrastive learning technique tailored for ZSL in the biomedical domain.
• Developed a method to generate semantically meaningful contrastive pairs by exploiting inherent context in biomedical text, unlike random segmentation strategies.

Main Results:

• The proposed context-aware contrastive learning approach significantly improves zero-shot biomedical text classification accuracy.
• Experiments on a large biomedical corpus demonstrated the effectiveness and superiority of the new method.

Conclusions:

• Zero-shot learning, particularly with context-aware contrastive learning, offers a promising solution for biomedical text classification challenges.
• This method reduces reliance on expensive labeled datasets, enabling broader application in biomedical research.