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Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Evaluation of Neural Network Transformer Models for Named-Entity Recognition on Low-Resourced Languages

DOI: http://dx.doi.org/10.15439/2021F7

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 115119 ()

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Abstract. In this paper, transformer models are used to evaluate ten low-resourced South African languages for NER. Further, these transformer models are compared to bi-LSTM-aux and CRF models. The transformer models have the highest F-score of 84%. This result is significant within the context of the study, as previous research could not achieve F-scores of 80%. However, the CRF and bi-LSTM-aux models remain top performers in sequence tagging. Transformer models are viable for low-resourced languages. Future research could improve upon these findings by implementing a linear-complexity recurrent transformer variant.


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