The DOI matches its Crossref record; 212 of the 220 references it cites were found in a registry or an index.
Bridging the gap: A survey of document retrieval techniques for high-resource and low-resource languages
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In the citing paper
“applying RAG to lowresource languages presents unique challenges, including poor embedding representations, low retrieval quality, and semantic gaps caused by the scarcity of digital documents.”
From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Kazi et al., 2025
In the citing paper
“the scarcity and uneven quality of digital corpora reduce retrieval recall and coverage (Kazi et al., 2025).”
From Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation (2026) · cites this paper as Kazi et al., 2025
Citations
Cites
Items 201–212 of 212
Computational Intelligence in Healthcare Informatics
Acharjya, D. P., & Ma, K. (2024) · Studies in computational intelligence · doi:10.1007/978-981-99-8853-2 · Cited by 2
Using small language models as tools for teaching in the ELSEI master's program
Aammou, S., Tagdimi, Z., & Tarik, T. (2024) · Cadernos de Educação Tecnologia e Sociedade · doi:10.14571/brajets.v17.nse4.84-95 · Cited by 2
Machine Translation for Low-Resource Languages: a community-based participatory approach
Weeks, C. A. (2021) · Cited by 1
Torres, D., del Pino, M., Gamboa, S., Antonelli, L., Martín, J., Lombardelli, M. J., & Fernández, A. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-70745-2_4 · Cited by 1
Searching Legal Information Using a Natural Language
Peltzel, J. (2016) · Studia Iuridica · Cited by 1
Domain Adaptation of Multilingual Semantic Search -- Literature ReviewPreprint
Bringmann, A., & Zhukova, A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.02932 · Cited by 1
Dhakal, S., Sigdel, S., Paudel, S. P., Ranabhat, S. K., & Lamichhane, N. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.05721 · Cited by 1
Cahyawijaya, S., Zhang, R., Lovenia, H., Cruz, J. C. B., Gilbert, E., Nomoto, H., & Aji, A. F. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.21573 · Cited by 1
The 2021 Tokyo Olympics Multilingual News Article DatasetPreprint
Novak, E., Calcina, E., Mladenić, D., & Grobelnik, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.06648 · Cited by 1
Advancing the Arabic WordNet: Elevating Content Quality
Freihat, A. A., Khalilia, H. M., Bella, G., & Giunchiglia, F. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3sodtwuwrss4 · Cited by 1
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Workshop, B., :, Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., et al. (2022) · arXiv · doi:10.48550/arxiv.2211.05100
Flagged first, then by citations
8 of the 220 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
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- Lancaster, 1973, Information retrieval on-line, Melville Publ, Co. Los Angel. Calif, 15
- Thomas Roelleke, Jun Wang, Probabilistic logical modelling of the binary independence retrieval model, in: Proceedings of the 1st International Conference on Theory of Information Retrieval (ICTIR 07)-Studies in Theory of Information Retrieval, 2007.
- Robertson, 1995, Okapi at TREC-3, Nist Spec. Publ. Sp, 109, 109
- Radford, 2019, Language models are unsupervised multitask learners, OpenAI Blog, 1, 9
- Nadir Durrani, Hassan Sajjad, Alexander Fraser, Helmut Schmid, Hindi-to-Urdu machine translation through transliteration, in: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 465–474.
- Samreen Kazi, Maria Rahim, Shakeel Ahmed Khoja, Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering, in: Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, 2025, pp. 141–151.
- Samreen Kazi, Shakeel Ahmed Khoja, Context-Aware Question Answering in Urdu, in: Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024), 2024, pp. 233–242.
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Cited by
Sorted by citations
Jotie, A. A., & Mitra, P. (2026) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3hbm9z4okcrx
UQuAD+: Benchmark Dataset for Urdu Machine Reading Comprehension
Kazi, S., & Khoja, S. A. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3759455
Self-citationAttention based neural network for cross domain fake news detection in Turkish language
Akpinar, K. O., Akpınar, M., & Pavlovskaya, O. (2025) · Scientific Reports · doi:10.1038/s41598-025-31788-3
MSGRNet: Self-supervised contrastive learning for radar signal modulation recognition and hashing
Chen, L., Cheng, J., & Li, M. (2026) · Science China Technological Sciences · doi:10.1007/s11431-026-3366-5
Jotie, A. A., & Mitra, P. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808532
Metadata
- Authors
- Kazi, S., Khoja, S., & Daud, A.
- Affiliations
- Institute of Business Administration Karachi (PK)
- Rabdan Academy (AE)
- Corresponding author
- Daud, Ali
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 57
- Pages
- 100756
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W4409482736
- Cited by
- 9
- References
- 220
- Citations by year
- 2026: 6 · 2025: 3
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computer Science Review (publishedVersion)
- Rabdan Academy Digital Repository (Rabdan Academy) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- document retrieval, low-resource languages, high-resource languages, cross-lingual information retrieval, domain adaptation, hybrid retrieval, neural ranking, morphological complexity, orthographic variation, data scarcity
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