Abstract: Automatic extraction of causal linkages from textual data has become important for knowledge-based reasoning and decision-support system applications. Identifying cause and effect relationships in unstructured text is used to enhance decision-making and textual knowledge interpretability. The goal of this research is to combine rule-based frameworks with deep learning methods to provide a novel method for reasoning and causality extraction from text. Specifically, it improves causal relation identification using the RoBERTa transformer model. Our approach entails extending RoBERTa to identify advanced causal relations, situational dependences, and cause-effect pairs from text data. Moreover, to improve the cognitive capacity and performance of text-derived knowledge, we’ve created a method of reasoning that reveals implicit causal knowledge. Our approach improves current causality extraction methods in terms of accuracy and durability, as shown by thorough evaluation on standard datasets. The model effectively identifies complex causal relationships, outperforming conventional approaches. This research advances the field of textual reasoning by providing a scalable and efficient framework for causality extraction and automated reasoning. The proposed method not only enhances accuracy but also opens new possibilities for real-world applications in decision support and knowledge-based systems.

Key words: causality extraction, textual reasoning, cause-effect pairs, RoBERTa, transformer based models