Comparative Analysis of Deep Learning Models for Part of Speech Tagging in the Malay Language

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 effectivene...

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Bibliographic Details
Published in:	HighTech and innovation journal Vol. 5; no. 2; pp. 272 - 281
Main Authors:	Adebayo, Bakare Mustaphaa, Anbananthen, Kalaiarasi Sonai Muthu, Muthaiyah, Saravanan, Lurudusamy, Saravanan Nathan
Format:	Journal Article
Language:	English
Published:	Ital Publication 01-06-2024
Subjects:	deep learning malay pos tagger malay text part of speech tagging
Online Access:	Get full text
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Summary:	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. Doi: 10.28991/HIJ-2024-05-02-04 Full Text: PDF
ISSN:	2723-9535 2723-9535
DOI:	10.28991/HIJ-2024-05-02-04