The development of human language technologies (HLTs) for these languages could contribute significantly to multilingualism and language development and ensure that South Africans are not excluded from benefits of improved human-machine interaction. Several legislative frameworks such as the National Language Policy Framework ( ), Language in Education Policy ( ), Language Policy Framework for South African Higher Education ( ), and the use of Official Languages Act (12/2012) promote the use and development of the official languages, especially the previously marginalized Southern Bantu languages. The neural network models evaluated perform better than the baselines for compound analysis, are viable and comparable to the baseline on most languages for POS tagging and NER, and are viable, but not on par with the baseline, for Afrikaans lemmatization. ![]() Neural architectures that performed well on similar tasks in other settings were implemented for each task and the performance was assessed in comparison with currently used machine learning implementations of each technology. The technologies we address are part of speech tagging, named entity recognition, compound analysis and lemmatization. ![]() In this paper, we address this gap by evaluating neural network implementations of four core technologies for ten South African languages. However, in the South African context, where most languages are resource-scarce, very little research has been done on neural network implementations of core language technologies. ![]() Neural networks are increasingly being used in place of other machine learning methods for many natural language processing tasks with good results. In this paper, the viability of neural network implementations of core technologies (the focus of this paper is on text technologies) for 10 resource-scarce South African languages is evaluated.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |