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.

The present study seeks to extract stylistic features of Qur’anic verses that address natural phenomena, some of which commentators have attempted to explain in scientific terms. To this end, seventy verses containing references to natural phenomena that had been claimed to constitute scientific miracles were selected. In addition, thirty verses were chosen which describe natural phenomena but from which no falsifiable propositions can be derived. As a third category, fifty verses whose central theme is supernatural were chosen for comparison. Consequently, three categories of verses were formed and, for brevity, labeled scientific verses, natural verses, and supernatural verses. Through an examination of existing scholarship on scientific verses, features potentially serving as stylistic markers of this category were identified. A corpus of 150 verses representing the three groups was then compiled and their features annotated. To analyze this corpus and extract distinctive stylistic traits of the first category, computational methods were applied, specifically a decision-tree analysis. The findings highlight linguistic characteristics of the Qur’an in its description of natural phenomena. The features with the highest probability of occurrence in scientific verses are, in descending order: multiple possible syntactic analyses, greater verse length, the presence of thought-provoking expressions, the use of augmented verb forms ( al-abwāb al-mazīd ), greater variety of letters, a higher number of derivational forms, and polysemy. A common underlying implication of these traits is that such verses require further inquiry and interpretive effort to construct a meaningful understanding. The study demonstrates that verses providing precise and falsifiable descriptions of natural phenomena display distinctive stylistic features that deserve further attention, ideally through interpretive approaches that are specifically sensitive to these linguistic markers.

As data and context continue to expand, a vast amount of textual content, including books, blogs, and papers, is produced and distributed electronically. Analyzing such large amounts of content manually is a time-consuming task. Automatic detection of feelings and emotions in these texts is crucial, as it helps to identify the emotions conveyed by the author, understand the author's writing style, and determine the target audience for these texts. The Qur’an, regarded as the word of God and a divine miracle, serves as a comprehensive guide and a reflection of human life. Detecting emotions and feelings within the content of the Qur’an contributes to a deeper understanding of God's commandments. Recent advancements, particularly the application of transformer-based language models in natural language processing, have yielded state-of-the-art results that are challenging to surpass easily. In this paper, we propose a method to enhance the accuracy and generality of these models by incorporating syntactic features such as Parts Of Speech (POS) and Dependency Parsing tags. Our approach aims to elevate the performance of emotion detection models, making them more robust and applicable across diverse contexts. For model training and evaluation, we utilized the Isear dataset, a well-established and extensive dataset in this field. The results indicate that our proposed model achieves superior performance compared to existing models, achieving an accuracy of 77% on this dataset. Finally, we applied the newly proposed model to recognize the feelings and emotions conveyed in the Itani English translation of the Qur’an. The results revealed that joy has the most significant contribution to the emotional content of the Holy Qur’an. 

Proving the miraculous nature of the Qur’an as one of the methods to believe in the Qur’an as a divine text has always attracted the attention of the researchers of the Qur’an; nevertheless, the published efforts on this subject are facing numerous methodological challenges. This research attempts to introduce a new and original method exempt from the challenges of the present methods by utilizing some of the techniques in mathematical sciences, logic and statistics. This scientific case-based method rates the effective parameters in the recognition of scientific miracle for one case. Accordingly, it will calculate the certainty of each case and proceeds to combine the ratings of various cases of scientific miracle to calculate the degree of certainty of the divine nature of the book based on those results. In combining the ratings, a threshold will be determined based on human belief behavior. An advantage of this method is that it is extra-religious, data driven, quantitative, applicable to all texts, and generalizable to the entire content. 

Some religious modernists believe that correct and scientific comprehension of the Qur’an is merely provided through historical and cultural study of the context of revelation time. Others, on the other hand, advocate that knowing interior and connected evidence suffices for Qur’an comprehension. Further, they say that exterior and unconnected evidence such as cultural and historical studies can have little effect on Qur’an comprehension. These various attitudes made us research them and represent a model to assess the dependency of Qur’an comprehension on cultural and historical conditions of revelation time. The present study examined works of scholars in this field such as Nasr Hamid Abu Zayd, Khorramshahi, and Tabatabaei through a descriptive and analytical method. Then, a model was recommended to evaluate the dependence of understanding of the Qur’an on the cultural and historical conditions of revelation time. The model includes four components: cause of revelation; vocabulary; subject matter; expressions which encompass twelve rules based on the features of verses in each component. Input of the model is one verse and its output is a dependent, independent, or semi-dependent mode.