Jul 2026· Iraqi Literary and Cultural Review (ILCR)· Vol 4, pp. 114-126· 0 citations· 10 references
TL;DR
It is argued that NLP becomes most valuable for the Iraqi humanities when computational methods are combined with cultural knowledge, philological care, translation ethics, and close literary interpretation.
Abstract
ABSTRACT
Objectives: This article examines how natural language processing can support the study, translation, preservation, and interpretation of Iraqi cultural texts. It argues that NLP becomes most valuable for the Iraqi humanities when computational methods are combined with cultural knowledge, philological care, translation ethics, and close literary interpretation.
Methods: The study adopts a qualitative and conceptual methodology informed by Arabic NLP, digital humanities, corpus linguistics, translation studies, and heritage informatics. It proposes an applied framework for building Iraqi cultural corpora, processing multilingual and historically layered texts, supporting human-in-the-loop translation, and analyzing literary and cultural patterns across large collections. The framework emphasizes corpus curation, metadata design, linguistic annotation, dialect sensitivity, and collaboration between computational specialists and humanities scholars.
Results: The analysis shows that NLP can improve access to Iraqi cultural materials through OCR, text normalization, named-entity recognition, topic modeling, terminology extraction, corpus alignment, machine-assisted translation, and computational literary analysis. These tools can help identify recurrent themes, places, figures, genres, and linguistic patterns. However, the article also identifies major challenges, including dialectal variation, script inconsistency, low-resource languages, cultural ambiguity, weak metadata, fragmented archives, and the risk of decontextualized algorithmic reading.
Conclusions: NLP should be treated not as a replacement for humanistic scholarship, but as a computational extension of it. For Iraqi cultural texts, sustainable progress requires curated corpora, multilingual data policies, ethical governance, translator review, heritage-sensitive metadata, and cooperation among universities, museums, libraries, archives, and local communities, while preserving interpretive depth, textual specificity, and cultural responsibility in future research infrastructures across Iraq today.
The paper argues that NLP should operate as an interpretive assistant rather than an autonomous literary translator in translating Iraqi poetry into English, and proposes a culturally aware, human-in-the-loop framework for supporting literary translation.
Whaj Mneer Esmail· Iraqi Literary and Cultural...· 0 citations
Amid deepening global cultural exchange and rapid advances in translation technology, French literary translation still faces challenges such as metaphor distortion, insufficient literary expression, and inconsistent rendering of culturally loaded terms. Current translation software often fails to balance semantic accuracy and literary quality because of insufficient targeted corpus support and weak adaptation algorithms for literary translation. This study proposes a systematic path for corpus construction and translation software optimization tailored to French literature. A dedicated bilingual parallel corpus is constructed by integrating classic literary works, authoritative translations, and cultural annotations. The corpus contains one million original French words and 800,000 parallel words, covering major literary genres and schools including Classicism, Romanticism, Realism, Modernism, and Existentialism. Based on corpus data, software optimization algorithms are designed across four dimensions: vocabulary alignment, syntactic structure parsing, cultural image mapping, and literary style adaptation. The study provides corpus-based technical support for improving translation accuracy, cultural adaptability, and stylistic fidelity in French literary translation.
Large language models are increasingly being incorporated into translation workflows, yet their performance in politically sensitive texts remains underexplored. Political discourse poses particular challenges for machine translation because it relies heavily on specialized terminology, ideological meanings, and context-dependent expressions. Drawing on Chapter Three of The Report on China’s Right to Development, this paper examines translation errors in ChatGPT-generated output through a comparison with revised human translations. The analysis identifies three recurring problem areas: unnatural linguistic choices, weak information organization, and non-standard rendering of political terms. These problems are traced to limitations in probabilistic text generation, insufficient domain-specific knowledge, and incomplete contextual interpretation. To address them, three post-editing strategies are proposed: register adjustment, structural reorganization, and terminology standardization. The findings suggest that while ChatGPT is capable of producing fluent drafts, high-quality translation of political texts still depends on human expertise. The study highlights the continuing importance of post-editing in ensuring linguistic accuracy, conceptual precision, and discourse appropriateness in AI-assisted political translation.
Lu Zhou· International Journal of Eng...· 0 citations
This study aims to examine how digital humanities methodologies, particularly natural language processing and network analysis, have influenced archival scholarship across Chinese- and English-language contexts from 2004 to 2023.
The study uses latent Dirichlet allocation for topic modeling, term frequency-inverse document frequency for keyword extraction and social network analysis to examine thematic patterns in archival journal articles. Chinese knowledge information processing tagger is used for Chinese-language data, while Gensim with Chinese and Gensim natural language toolkit is applied to English-language data, enabling cross-linguistic comparison of thematic patterns.
The results reveal distinct thematic divergence. Chinese-language journals emphasize state-led initiatives, such as national identity construction and digital infrastructure, whereas English-language journals focus on community archives, Indigenous rights and archival justice. Despite these differences, both corpora converge on themes such as digital archive management, policy dissemination and archival education, reflecting a shared emphasis on archival infrastructure and governance. Topic modeling identifies six coherent themes in Chinese articles and seven in English. Keyword analysis shows that Chinese literature prioritizes institutional roles, whereas English texts emphasize social justice and community engagement.
The study’s primary contribution lies in its cross-linguistic comparative design, which integrates topic modeling, keyword clustering and social network analysis across Chinese- and English-language scholarly corpora. This approach highlights divergent orientations in archival knowledge production across the two communities, a dimension that has received limited systematic attention in prior single-language or single-method studies.
Chyi Wang, Chiao-Min Lin· Global Knowledge Memory and...· 0 citations
Translation is defined as the process of transferring meaning from one language to another. It is an extremely difficult and complex process because it involves not only transferring words but also ideas, culture, linguistic customs, and meanings derived from syntactic elements, their arrangement, word structure, and derivation. This is especially true in languages with complex structures, such as Arabic, which is characterized by its multiple linguistic contexts. These contexts have not been adequately addressed by NLP (Natural Language Processing) applications in machine translation models due to the lack of diverse contexts where metaphor, figurative language, grammatical inflections, morphological patterns and their connotations, and sentence structure all play pivotal roles in determining meaning. Furthermore, spoken language, with its inherent phonetic and expressive characteristics, conveys the text into broader semantic spaces. These spaces are influenced by the effect of intonation on specific syllables, the speaker's psychological state, the listener's mood, and accompanying body language, which transforms meaning into other subtle details. All of this, and more, is absent from machine translation, no matter how hard its creators try to imbue it with human emotions and feelings. This study highlights the importance of integrating in-depth linguistic analysis, contextual semantic modelling, and cultural awareness into natural language processing-based translation systems. By combining traditional linguistic insights with computational methods, the research offers a framework that can contribute to improving the accuracy of machine translation from Arabic to English.
Hilal Abdul-Raziq Sadiq, Zaxid Maxmudovich Islamov, R. Matibaeva et al.· Digital Technologies Researc...· 0 citations
Ambiguity is widely recognized as one of the fundamental challenges in natural language processing. It significantly affects both human language comprehension and computational language interpretation. Among its various forms, structural and textual ambiguity present particular difficulties for linguistic analysis and artificial intelligence systems. This study investigates the linguistic characteristics of structural and textual ambiguity in English and examines how contemporary language models process and interpret ambiguous linguistic structures.The research is based on a corpus of selected a three authentic English sentences containing structural and textual ambiguity. An experimental study was conducted with 45 students at Azerbaijan University of Languages. In the first phase, structurally ambiguous sentences were analyzed using the PRAAT computer program to examine their linguistic and prosodic features. In the second phase, students were instructed to generate texts in ChatGPT using the selected structurally ambiguous sentences as prompts. The resulting texts were subsequently analyzed and compared with outputs generated by ChatGPT, Gemini and Claude to investigate how these artificial intelligence systems interpret, contextualize and resolve structural ambiguity at the textual level.The findings provide insights into the mechanisms employed by large language models in processing ambiguous language and demonstrate similarities and differences in their contextual interpretation of structurally ambiguous constructions. The study contributes to the theoretical understanding of structural and textual ambiguity highlighting their communicative functions in linguistics, natural language processing and artificial intelligence.The relevance of the study lies in its interdisciplinary approach, integrating theoretical linguistics, experimental research, and AI-based language analysis. The results contribute to the growing body of research on ambiguity resolution and offer practical implications for the development of more accurate and context-sensitive natural language processing systems.
Khanbutayeva Leyla Musa· Aposta: Revista de Ciencias...· 0 citations