Deep Learning transformer architecture for Named Entity Recognition on low resourced languages: state of the art results

• Autorzy: Hanslo Ridewaan

Identyfikatory

DOI

10.15439/2022F53

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named-Entity Recognition (NER) on ten low-resourced South African (SA) languages. In addition, these DL transformer models were compared to other Neural Network and Machine Learning (ML) NER models. The findings show that transformer models substantially improve performance when applying discrete fine-tuning parameters per language. Furthermore, fine-tuned transformer models outperform other neural network and machine learning models on NER with the low-resourced SA languages. For example, the transformer models obtained the highest F-scores for six of the ten SA languages and the highest average F-score surpassing the Conditional Random Fields ML model. Practical implications include developing high-performance NER capability with less effort and resource costs, potentially improving downstream NLP tasks such as Machine Translation (MT). Therefore, the application of DL transformer architecture models for NLP NER sequence tagging tasks on low-resourced SA languages is viable. Additional research could evaluate the more recent transformer architecture models on other Natural Language Processing tasks and applications, such as Phrase chunking, MT, and Part-of-Speech tagging.

Słowa kluczowe

EN

named-entity recognition; NER; natural language processing; neural network; sequence tagging; XLM-R; machine learning; transformer model

PL

NER; przetwarzanie języka naturalnego; sieć neuronowa; XLM-R; uczenie maszynowe; model transformatora

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 53--60
• Opis fizyczny: Bibliogr. 21 poz., tab., wykr., wz.

Twórcy

autor

Hanslo Ridewaan

• University of Pretoria Gauteng, South Africa

Bibliografia

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Uwagi

1. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).