Year: 2025
Type: article
Abstract: The problem of Named Entity Recognition (NER) for Igbo language, one of Nigeria’s major languages still exists because of limitations in Natural Language Processing (NLP) tools. This paper reviews progress in Named Entity Recognition (NER) for low-resource African languages, with focus on Igbo. NER is a key task in NLP that involves identifying entities such as persons, locations, organizations, and dates. While high-resource languages have benefited from large datasets and advanced models, African languages face difficulties due to limited corpora, complex grammar, and inconsistent writing standards. Recent efforts, including MasakhaNER and IgboNER 2.0, have begun to address these gaps by releasing annotated datasets and baseline systems. Transformer models such as XLM-R and AfriBERTa have further shown that multilingual pretraining can improve recognition in Igbo, though challenges remain in annotation quality and coverage of entity types. This review highlights advances in dataset development, annotation practices, and the use of multilingual models, and shows how extending Igbo NER to five entity types with XLM-R can strengthen both research and application.
Authors: Chika-Ugada, Precious Kelechukwu, Odirichukwu, Jacinta Chioma, Nnamdi, Reginald Nnadozie, Odirichukwu, Simon Peter Chimaobi, Ndigwe, Chinwe, Njoku, Obilor Athanasius, Atolagbe, Oluwatobi Wisdom, Dimoji, Chigozie, Ahubele, Betty Osamegbe, Nwibo, Ezekiel Gabriel, Inyang, Iriagbonse Amanda, Okosun, Oduware, Nwoke, John Chinenye, Dunu, Chiedozie Raphael, Dinneya, Joshua Nzubechukwu, Diala, Felix Nmesoma, Dialaeme-Diolulu, Samuel Chizitaram, Liberty, Chukwuka Prince, Kanu, Divine Favour, Uzodinma, John Prince
Institutions: Federal University of Technology Owerri, National Primary Health Care Development Agency, Chukwuemeka Odumegwu Ojukwu University, Energy Storage Systems (United States), Benson Idahosa University, University of Greater Manchester, Creative Technologies (United States), University of Benin, Rivers State University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219982011
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