علیرضا طالب پور

This study aims to examine the Qur’anic verses that record the dialogues between the Prophet Muhammad (PBUH) and the people. After identifying the dialogical methods employed by the Prophet’s audience, it investigates how the Prophet responded to both opponents and supporters according to their methods. To achieve accurate results and provide a systematic analysis of the verses, association rules (a data-mining method) have been applied. The findings show that the Holy Qur’an contains 153 verses devoted to the dialogues of the Prophet with the people, which can be divided into 72 separate conversations. The main focus of this study is on the verses in which the people initiate the dialogue and the Prophet provides the response. By categorizing the Prophet’s interlocutors into opponents and supporters, and by identifying the dialogical methods used by both sides, ten dialogical methods were recognized: emotional, argumentative, interrogative, exhortative, persuasive, propagative, denunciatory, assertive, derisive, and threatening. The analysis reveals that when the opponents initiate dialogue in an interrogative manner, the Prophet’s response is propagative; when their method is denunciatory, the Prophet responds with propagative and argumentative approaches; when their method is assertive, the Prophet responds in a persuasive manner; and when their method is derisive, his response is threatening. By contrast, the dialogue method of  the supporters is predominantly interrogative, to which the Prophet responds in an exhortative manner.

The Recent advancements in deep learning have yielded novel and significant capabilities in natural language processing (NLP) and automatic inference generation. These capabilities are particularly critical due to their resemblance to human reasoning. At the same time, interdisciplinary initiatives have led to substantial advancements in the realms of knowledge and technology. In this study, the Qur’an is examined as a rich source of multiple concepts and teachings. The objective of this research is to employ natural language processing algorithms to derive meaningful and accurate inferences from the English translation of the Qur’an. Inference is defined as the process of deriving a new and logical sentence from two basic and related sentences. The research methodology introduces a model that utilizes transformers and pre-trained language models. Consequently, we construct the set of all unique unordered verse pairs (i<j) from 6,236 verses, totaling 19,440,730 pair evaluations. A fine-tuned BERT-based classifier labels each pair as either exhibiting or not exhibiting a syllogistic relation. Pairs predicted as “Yes” and exceeding a confidence threshold of 0.80 proceed to the subsequent stage, which is inference generation, while all other pairs are discarded. In the following phase, large language models are employed to generate inferences from the selected pairs of verses.