Named entity recognition for African languages : a focus on the Igbo language
Bibliographic record
Abstract
Named Entity Recognition (NER) is a crucial task for many downstream NLP applications, including text summarization, document indexing, question answering, classification, and machine translation. Analysis of research reveals that 95% NLP efforts are concentrated on English and a few other languages like Japanese, German, and French, even though there are over 7,000 languages globally. Around 90% of African languages are considered under-resourced in NLP highlighting the gap in resources for African languages The work presented in this thesis significantly advances Named Entity Recognition (NER) for low-resource languages, particularly African languages like Igbo, which, despite having millions of speakers, has remained largely underrepresented in NLP research. Focusing on Igbo, this research addresses a critical gap where foundational tools and resources, such as IgboNER, have been unavailable, thus limiting the language’s integration into broader computational applications. Prior to this work, the Igbo language lacked dedicated NER resources and a specialised language model essential for accurate information extraction and analysis, which has kept Igbo on the periphery of digital advancements in NLP. To address this gap, we developed IgboBERT, the first transformer-based language model pre-trained from scratch on the Igbo language, to serve as a baseline model. We created a parallel English-Igbo corpus and utilized spaCy, an existing NER tool for the high-resource English language, to tag the English sentences. These tags were then transferred to Igbo using a projection method, aided by our semiautomatically created mapping dictionary to facilitate the tag transfer process. Additionally, we designed a framework for the creation of the IgboNER dataset, which can be extended to other low-resource languages. We fine-tuned IgboBERT and several state-of-the-art models, including mBERT, XLM-R, and DistilBERT, for the downstream IgboNER task using transfer learning. Our evaluation across various data sizes indicated that while large transformer models significantly benefited the IgboNER task, fine-tuning a transformer model built from scratch with relatively little Igbo text data also produced commendable results. This work substantially contributes to IgboNLP and the broader African and low-resource NLP landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".