AUTHORS: Irfan ALI, Liliana LO PRESTI, Igor SPANO’, Marco LA CASCIA

URL: https://link.springer.com/chapter/10.1007/978-3-032-04627-7_4

Work Package: WP4 – DaMSym

Keywords: Word Segmentation, Sanskrit Language, Sandhi Rule, Bi-Encoders, Attention.

Abstract

This paper proposes a novel deep learning-based multi-task approach designed to handle Sanskrit compound word splitting, a problem that poses significant challenges due to the complicated morphology of the language. The model is trained to jointly solve two closely related tasks: character-level segmentation location prediction and segmented word generation using a sequence-to-sequence model. The proposed architecture consists of two branches and relies on a shared character-level embedding layer combined with bidirectional LSTMs to effectively capture character-level contextual variations. Furthermore, each branch leverages the Multi-Head Attention mechanism, and experiments show that this mechanism improves character-level segmentation location prediction and guides the decoder when generating segmented words. Differently than state-of-the-art approaches, adopting cascaded models for solving independently the two tasks, in the proposed unified model, the decoder is initialized with a composite state that combines the encoder context with the location features computed by the segmentation branch, allowing the two tasks to support each other and leading to improvements in the overall model accuracy. Experiments on a challenging Sanskrit compound dataset show that the proposed model outperforms traditional methods, achieving a location prediction accuracy of  and a splitting accuracy of , respectively. The experiments further show that joint modeling of the two tasks not only advances the state-of-the-art performance in splitting Sanskrit compounds, but also offers a richer understanding of the splitting process, paving the way for strengthening morphological analysis in natural language processing and digital humanities for the in-depth study of complex religious texts.

AUTHORS: Amina EL GANADI, Luca GAGLIARDELLI and Federico RUOZZI

URL: https://link.springer.com/article/10.1007/s00799-025-00432-w

Work Package: WP5 – Digital Maktaba

Keywords: Arabic digital library, Bibliographic metadata, Digital maktaba project, OCR, Cultural heritage, LLMs

Abstract
The rapid digitization of cultural heritage has underscored the critical need for robust digital libraries, particularly for underrepresented languages like Arabic and Persian. This paper describes the methodologies and challenges involved in developing a metadata-driven Arabic digital library, utilizing bibliographic metadata extracted from the Diamond catalogue. It explores advanced metadata schemas, such as Dublin Core, and integrates text recognition technologies and preservation strategies to address key concerns of accessibility, scholarly use, and the long-term preservation of Arabic-script texts. The paper delves into specific challenges of processing Arabic script, including handling calligraphy, diacritics, and ligatures, and introduces innovative solutions like the use of frontispiece images to train OCR systems. Furthermore, it discusses how integrated metadata could not only enhance text recognition but also improve user engagement by enabling refined search functionalities and better resource discovery. Finally, the paper outlines future directions for expanding metadata frameworks to ensure interoperability and the long-term preservation of cultural heritage.

AUTHORS: Amina EL GANADI, Luca GAGLIARDELLI, Sania AFTAR and Federico RUOZZI

URL: https://ceur-ws.org/Vol-3937/short13.pdf

WORK PACKAGE: WP5 – Digital Maktaba

Keywords: Document Analysis, Arabic Digital Library, Bibliographic Metadata, Digitization, Digital Maktaba Project, OCR, Cultural Heritage, Natural Language Processing.

Abstract
The rapid digitization of cultural heritage has underscored the critical need for robust digital libraries, particularly for underrepresented languages like Arabic and Persian. This paper describes the methodologies and challenges involved in developing a metadata-driven Arabic digital library, utilizing bibliographic metadata extracted from the Diamond catalogue. It explores advanced metadata schemas, such as Dublin Core, and integrates text recognition technologies and preservation strategies to address key concerns of accessibility, scholarly use, and the long-term preservation of Arabic-script texts. The paper delves into specific challenges of processing Arabic script, including handling calligraphy, diacritics, and ligatures, and introduces innovative solutions like the use of frontispiece images to train OCR systems. Furthermore, it discusses how integrated metadata could not only enhance text recognition but also improve user engagement by enabling refined search functionalities and better resource discovery. Finally, the paper outlines future directions for expanding metadata frameworks to ensure interoperability and the long-term preservation of cultural heritage.

AUTHORS: Amina EL GANADI, Sania AFTAR, Luca GAGLIARDELLI and Federico RUOZZI

URL: https://www.scitepress.org/PublicationsDetail.aspx?ID=sZzObYxusMU=&t=1

WORK PACKAGE: WP5 – Digital Maktaba

Keywords: Generative AI Applications, Digital Humanities, Hallucinations, Religious Text Analysis, Bias Mitigation, Context-Aware Constraints, Prompt Engineering, Large Language Models (LLMs), GPT Builder, AI in Islamic Studies, Hadith Studies, Sahih Al-Bukhari.

Abstract
Recent advancements in large language models (LLMs) have facilitated specialized applications in fields such as religious studies. Customized AI models, developed using tools like GPT Builder to source information from authoritative collections such as Sahih al-Bukhari or the Qur’an, were explored as potential solutions to address inquiries related to Islamic teachings. However, initial evaluations highlighted significant limitations, including hallucinations and reference inaccuracies, which undermined their reliability for handling sensitive religious content. To address these limitations, this study proposes EMAN (Embedding Methodology for Authentic Narrations), a novel framework designed to enhance adherence to Sahih al-Bukhari through API-based integration. Three methodologies are examined within this framework: Zero-Shot Instructions, which guide the model without prior examples; Few-Shot Learning, which fine-tunes the model using a limited set of examples; and Embedding-Based Integration, which grounds the model directly in a verified Ahadith database. Results demonstrate that Embedding-Based Integration significantly improves performance by anchoring outputs in a structured knowledge base, reducing hallucination rates, and increasing accuracy. The success of this approach underscores its potential for enhancing LLM performance in precision-critical domains. This research provides a foundation for the ethical and accurate deployment of AI in religious studies, emphasizing accountability and fidelity to source material.