Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation

2026-05-14 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computational Psychology · Depth: Expert, quick

Summary

A new context-aware synthetic augmentation framework, combined with a hybrid classification model, has been developed to address data scarcity and class imbalance in automatically classifying psychological defense mechanisms (PDMs) from text. This framework was specifically designed for the PsyDefDetect shared task (BioNLP@ACL 2026). The hybrid model integrates contextual language representations with basic clinical features and utilizes 150 annotated defense items. Experiments showed that the quality of definitions used in prompting directly impacts the fidelity of generated data and subsequent classification performance. This method achieved an accuracy of 58.26% and a macro-F1 of 24.62%, outperforming the DMRS Co-Pilot by +40.25% and +15.99% respectively, establishing a robust baseline for PDM classification in low-resource environments.

Key takeaway

For NLP engineers developing classification models in clinical psychology, your focus should be on integrating context-aware synthetic data augmentation with hybrid models. Prioritize high-quality, psychologically grounded definitions in your prompting to maximize generation fidelity and improve downstream performance, especially in data-scarce scenarios.

Key insights

Context-aware synthetic augmentation significantly improves psychological defense mechanism classification in low-resource settings.

Principles

• Definition quality in prompting governs generation fidelity.
• Hybrid models combine contextual language with clinical features.

Method

The proposed method uses a context-aware synthetic augmentation framework with a hybrid classification model, integrating contextual language representations and basic clinical features, guided by 150 annotated defense items.

In practice

• Use high-quality definitions for synthetic data generation.
• Integrate clinical features with language models for PDM tasks.

Topics

• Psychological Defense Mechanisms
• Data Scarcity Mitigation
• Context-Aware Synthetic Augmentation
• Hybrid Classification Models
• Low-Resource NLP

Code references

• htdgv/CASA-PDC

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.