The proposed hybrid framework for causality extraction from nuclear licensee event reports can be extended to extract causal relationships from other technical documents by adapting the methodology to suit the specific domain and language patterns of the new documents. This extension would involve:
Domain-specific Data Collection: Gather a new corpus of technical documents from the desired domain, ensuring that the dataset is representative of the types of causal relationships present in those documents.
Annotation and Labeling: Develop an interactive tool for annotating cause-effect pairs in the new documents, similar to the process described in the study for nuclear event reports.
Data Preprocessing: Clean and preprocess the text data from the new documents, ensuring consistency and removing unwanted characters or formatting issues.
Model Adaptation: Modify the deep learning model architecture to accommodate the language patterns and nuances specific to the new domain. This may involve adjusting the embedding layer, convolutional layer, and LSTM layers to capture relevant causal relationships.
Pattern-based Extraction: Define and refine causal patterns based on the characteristics of the new technical documents to accurately extract cause-effect pairs.
Evaluation and Validation: Test the adapted framework on the new dataset, evaluating its performance in detecting causal relationships and extracting cause-effect pairs accurately.