Exploring Contextual Similarity in Quranic Ayahs: A Case Study of Surah Al-Baqarah and Aal-e-Imran in Urdu Translations

The translation of sacred texts, particularly the Quran, requires a deep understanding of both linguistic and contextual nuances to preserve the original message. This research investigates the contextual similarity among Quranic Ayahs by analyzing the Urdutranslations of Surah Al-Baqarah and Aal-e-Imran from Maulana Maududi's Urdu Quranic translation. Given the importance of accurately conveying the essence of the original Arabic text, this study aims to quantify the contextual relationships between Ayahs within each Surah and assess the effectiveness of Maulana Maududi’s translation in maintaining these relationships.The novelty of this study lies in its application of deep learning, particularly Long Short-Term Memory (LSTM) networks, to evaluate the contextual similarity between Ayahs. The LSTM model is used to capture the deep linguistic and contextual relationshipswithin the translation, offering a data-driven approach to Quranic translation evaluation. The dataset comprises the complete translations of Surah Al-Baqarah and Aal-e-Imran in Urdu, and each Ayah is compared with every other Ayah within the same Surah to compute similarity scores.The results show varying degrees of similarity among Ayahs, with some Ayahs exhibiting high contextual alignment while others display subtle divergences. These findings highlight the ability of LSTM models to uncover hidden patterns in translation, while also pointing out the challenges in preserving the full contextual integrity of the original Arabic text in translation.In conclusion, this study provides valuable insights into the complexities of Quranic translation and offers a novel approach to evaluating the quality of such translations. Combining advanced machine learning techniques with the study of sacred texts presents a new avenue for improving the accuracy and contextual coherence of Quranic translations, ultimately contributing to the field of computational linguistics and religious studies