---
title: "Namibia — research institutions, scientists and papers"
description: "Namibia: 1 institutions with papers in our library in 1 agglomerations, 0 people with a page, 3 works, 917 citations."
canonical: "https://research.elamur.ai/free/en/countries/namibia/"
---
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# Namibia

Country 1 institution in 1 agglomeration · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**1** institution with a page](https://md.research.elamur.ai/free/en/countries/namibia/#institutions) · [**1** agglomeration](https://md.research.elamur.ai/free/en/countries/namibia/#agglomerations) · [**0** people with a page](https://md.research.elamur.ai/free/en/countries/namibia/#authors) · [**3** works in our library](https://md.research.elamur.ai/free/en/countries/namibia/#works) · [**917** citations of their works](https://md.research.elamur.ai/free/en/countries/namibia/#works)

## About

### Profile

Namibia appears on this site through a small, tightly focused body of research. All of it is connected to one institution, the [Namibia University of Science and Technology](https://md.research.elamur.ai/free/en/universities/namibia-university-of-science-and-technology/), a university in [Windhoek](https://md.research.elamur.ai/free/en/agglomerations/windhoek/). Windhoek is the only agglomeration recorded for the country here. The country can be compared with others through the list of [all countries](https://md.research.elamur.ai/free/en/countries/), and its [institutions](https://md.research.elamur.ai/free/en/countries/namibia/institutions/) and [agglomerations](https://md.research.elamur.ai/free/en/countries/namibia/agglomerations/) each have their own overview.

The picture on this site is narrow but coherent. The works connected to Namibia all belong to [natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/). More precisely, they deal with building language technology for African languages. Three threads run through them: recognising names and entities in text, translating between languages, and working with languages that have little digital text available. These works have been cited widely within the library. That says something about how much attention this line of research has drawn among the papers collected here. It does not describe the full range of science done in the country, which this site does not cover.

### History

The works in our library place Namibia's visible research activity in the early 2020s. The earliest of them dates from 2020. It is a study of participatory approaches to machine translation for African languages. In 2021 came a shared resource and benchmark for named entity recognition across many African languages. In 2022 came a study of how a modest amount of carefully translated news text can adapt large pre-trained translation models to African languages.

Taken together, the three works show a clear progression. The first sets out how research on under-served languages might be organised: who takes part, how data gets built, and why the existing gaps exist. The second puts that approach to work on a concrete language understanding task, with annotated data across a set of languages. The third goes back to translation and asks a practical question. Once a participatory community exists and some quality data has been gathered, how far can that data go when combined with large pre-trained models? On this site, then, the history of Namibian research is the history of one line of collaborative work on African language technology. It moves from method and community to datasets and then to model adaptation, all within a short span of years.

### Research profile

The single field recorded for Namibia here is natural language processing. Within it, three topics stand out, each represented by one work.

The first topic is [named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/). This is the task of finding and classifying names of people, places, organisations and similar items in running text. The work [MasakhaNER: Named Entity Recognition for African Languages](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/) addresses it for a group of African languages. Part of what sets this study apart is its attention to the languages themselves. The authors lay out the typology and writing conventions of each language, covering matters such as the scripts used and how tone is handled, before turning to annotation and modelling. This grounding in linguistic detail is a recurring feature of the research connected to Namibia on this site.

The second topic is [machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/). Two works touch on it. The participatory study from 2020 treats translation for African languages as a social as well as a technical problem. The 2022 study treats it as a problem of adapting models. The latter is centred on news translation. It examines how pre-trained multilingual systems, which learn mostly from text crawled from the web, can be improved for African languages that are poorly represented online. The answer it proposes is a relatively small set of high-quality human translations.

The third topic is [low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/). This is the common ground beneath all three works. Each starts from the observation that most African languages lack the large text collections that modern language technology relies on. Each responds by creating data, setting up evaluation benchmarks, or finding ways to make limited data count for more. Among the works here, the Namibian contribution sits squarely in this area of research.

### Leading scientists

No researcher associated with Namibia has an individual profile on this site. The people behind the work can still be seen through the author lists of the three papers.

David Ifeoluwa Adelani is the first author of two of the three works: the named entity recognition study of 2021 and the news translation study of 2022. Adelani's work here therefore covers both language understanding and translation for African languages. It is concerned throughout with building datasets and evaluating models on them. Jade Abbott and Graham Neubig appear among the authors of the named entity recognition work. Jesujoba Oluwadara Alabi and Angela Fan appear among the authors of the translation study.

Wilhelmina Nekoto is the first author of the 2020 participatory research paper, with Vukosi Marivate and Tshinondiwa Matsila among the co-authors. That work sets out the community-based approach that shapes the later studies. The works name many further co-authors beyond these. The papers are large collaborative efforts rather than the output of a single lab, and the people named here are the first few authors on each.

### Landmark works

The most cited work connected to Namibia on this site is [MasakhaNER](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/), from 2021. It introduced named entity resources for a set of African languages. It described each language's structure and orthography, including script and tone, and reported how well various models performed on the new data. Many papers in the library cite it, which suggests it has served as a reference point for later work on entity recognition in African languages.

Next is [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/), from 2020. Its central argument is that the shortage of language technology for African languages is not only a matter of missing data. The authors trace the gap to social and historical inequities. They make the case for research in which speakers, translators and researchers from the communities concerned take part directly at every stage. The paper is a methods and position piece as much as a technical one, and it is widely cited among the works here.

The third work is [A Few Thousand Translations Go a Long Way!](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/), from 2022. It starts from the fact that large multilingual translation models learn mostly from web text, so languages with little online presence are poorly served. The study shows that fine-tuning pre-trained models on a small collection of high-quality news translations can improve results considerably. That makes careful human translation a practical lever for under-resourced languages. It too has drawn many citations in the library.

### Labs and units

The only institution recorded for Namibia on this site is the Namibia University of Science and Technology in Windhoek, classed as a university. No departments, laboratories or research units are recorded separately. All three works are associated with the university as a whole, and it accounts for every citation attributed to the country here.

### Collaborations

The author lists show that the Namibian works come from large, multi-author collaborations rather than from a single local group. The named entity recognition paper and the news translation paper each bring together a long list of contributors. So does the participatory translation study. The shared focus on African languages, and the recurrence of authors such as Adelani across papers, point to a sustained network working on African language technology. The Namibia University of Science and Technology is one of its participating institutions.

The site does not record which co-authors belong to which other institutions or countries. It is therefore not possible to map these partnerships country by country. What the works do make clear is the character of the collaboration. It is community-driven, it spans many African languages, and it combines linguistic description, data creation and model evaluation.

### Timeline

2020: The first work connected to Namibia on this site appears. It is the participatory study of machine translation for African languages, led by Wilhelmina Nekoto. It frames under-resourced language research as both a social and a technical undertaking, and it argues for involving language communities directly.

2021: The named entity recognition resource for African languages follows, led by David Ifeoluwa Adelani. It becomes the most cited of the Namibian works here and extends the collaborative approach to a core language understanding task, with close attention to each language's structure and writing.

2022: The news translation study, again led by Adelani, rounds out the set. It shows that a few thousand quality translations can substantially improve large pre-trained translation models for African languages. With it, the line of work comes back to translation with a practical, data-efficient method.

## Where

## Agglomerations (1)

1. [**Windhoek**1 institution*917 citations*](https://md.research.elamur.ai/free/en/agglomerations/windhoek/)

## Institutions (1)

### 🎓 Universities (1)

**[Namibia University of Science and Technology](https://md.research.elamur.ai/free/en/universities/namibia-university-of-science-and-technology/)**University · [Windhoek](https://md.research.elamur.ai/free/en/agglomerations/windhoek/), Namibia*3 works · 917 citations*

## Works in our library (3)

Works of its institutions, the most cited first.

1. [**MasakhaNER: Named Entity Recognition for African Languages**David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, et al.*2021 · 429 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/)
2. [**Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages**Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, et al.*2020 · 333 citations*](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)
3. [**A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation**David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

💡 Concepts 1[Named entity recognition · 1 work · 20.7](https://md.research.elamur.ai/free/en/concepts/named-entity-recognition/)

📚 Shelves 1[Natural language processing · 3 works · 51.4](https://md.research.elamur.ai/free/en/shelves/nlp/)

🧭 Topics 3[Named entity recognition · 1 work · 20.7](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Low-resource languages · 1 work · 9.1](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · [Machine translation · 1 work · 9.1](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Works by year

### 2020s

[**2022** 1 work](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/) · [**2021** 1 work](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/) · [**2020** 1 work](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)

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