Citation

Adebayo, Bakare Mustaphaa and Sonai Muthu Anbananthen, Kalaiarasi and Muthaiyah, Saravanan and Lurudusamy, Saravanan Nathan (2024) Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language. HighTech and Innovation Journal, 5 (2). pp. 272-281. ISSN 2723-9535

Text Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language _ Adebayo _ HighTech and Innovation Journal.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Despite the widespread use of Malay, under-resourced languages like Malay face challenges in Natural Language Processing (NLP), particularly in Part-of-Speech (POS) tagging. The scarcity of annotated corpora poses a primary obstacle to POS tagging in Malay. This study aims to enhance the effectiveness and reliability of POS tagging models explicitly tailored for under-resourced languages within the field of NLP, focusing on Malay. Existing models, which rely on Conditional Random Fields and Hidden Markov Models, exhibit limitations, underscoring the need for more robust approaches. The research conducts a comparative analysis of various deep-learning models with different encoders for POS tagging in Malay sentences. The experimental analysis demonstrates that the Bidirectional Long Short-Term Memory (Bi-LSTM) model, leveraging a pre-trained Bidirectional Encoder Representations from Transformers (BERT) embedding model, achieves exceptional accuracy, precision, recall, and F1 scores in predicting tags. Notably, the BERT + Bi-LSTM model, boasting an accuracy of 98.82%, outperforms other models, showcasing superior performance across all evaluated metrics. Additionally, this combined model effectively handles known and unknown words, yielding highly accurate POS tagging results for Malay sentences

Item Type:	Article
Uncontrolled Keywords:	Part of Speech Tagging; Deep Learning; Malay Text; Malay POS Tagger
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Jul 2024 04:18
Last Modified:	31 Jul 2024 04:18
URII:	http://shdl.mmu.edu.my/id/eprint/12683

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