Natural Language Processing for African Languages in Tanzania: Challenges and Opportunities
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| Auteurs principaux: | , , , |
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2009
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| _version_ | 1866901411510353920 |
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| author | Chituwo, Munyenyumwa Shabanini, Nsimba Mwita, Kamadi Kigula, Simiyu |
| author_facet | Chituwo, Munyenyumwa Shabanini, Nsimba Mwita, Kamadi Kigula, Simiyu |
| contents | <p>Natural Language Processing (NLP) is a critical component of modern data science and machine learning. A systematic literature review was conducted to identify existing tools and frameworks used for NLP in Tanzanian languages. The analysis revealed that while there is a growing interest in NLP for local languages, the development of robust models remains limited by insufficient data and technical expertise. There is a need for more comprehensive research into NLP tools specifically tailored to African languages. Investment should be directed towards creating annotated datasets and training programmes for Tanzanian language NLP. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18887708 |
| institution | Zenodo |
| language | eng |
| publishDate | 2009 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Natural Language Processing for African Languages in Tanzania: Challenges and Opportunities Chituwo, Munyenyumwa Shabanini, Nsimba Mwita, Kamadi Kigula, Simiyu African languages Computational linguistics Data mining Machine learning Natural language processing Text analytics Vector spaces <p>Natural Language Processing (NLP) is a critical component of modern data science and machine learning. A systematic literature review was conducted to identify existing tools and frameworks used for NLP in Tanzanian languages. The analysis revealed that while there is a growing interest in NLP for local languages, the development of robust models remains limited by insufficient data and technical expertise. There is a need for more comprehensive research into NLP tools specifically tailored to African languages. Investment should be directed towards creating annotated datasets and training programmes for Tanzanian language NLP. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| title | Natural Language Processing for African Languages in Tanzania: Challenges and Opportunities |
| topic | African languages Computational linguistics Data mining Machine learning Natural language processing Text analytics Vector spaces |
| url | https://doi.org/10.5281/zenodo.18887708 |