The deep learning-based causality classification approach may face limitations in handling complex causal expressions, such as implicit and embedded causality, due to the following reasons:
Subtlety of Causal Relations: Deep learning models may struggle to capture subtle causal relationships expressed in text, especially when the cause and effect are not explicitly stated.
Data Imbalance: Imbalanced datasets with a higher proportion of non-causal samples can lead to misclassification of causal relationships.
Limited Training Data: The model's performance may be limited by the availability of annotated cause-effect pairs for training.
To improve the model's capability in handling complex causal expressions, the following strategies can be implemented:
Data Augmentation: Generate synthetic data to balance the dataset and expose the model to a wider range of causal expressions.
Enhanced Feature Extraction: Incorporate more advanced NLP techniques to extract implicit and embedded causality, such as attention mechanisms or transformer models.
Transfer Learning: Pre-train the model on a large corpus of text data to capture general causal relationships before fine-tuning it on the specific technical documents.
Ensemble Methods: Combine multiple deep learning models to leverage their strengths in capturing different types of causal expressions.
Human-in-the-Loop: Integrate human validation and feedback loops to refine the model's predictions and improve its performance on complex causal relationships.