Comparative Analysis of Part of Speech Tagging Methods for the Bugis Language: From Statistical to Deep Neural Approaches

Abstract

Part-of-speech (POS) tagging is essential in natural language processing (NLP) that facilitates various downstream applications. However, POS tagging for low-resource languages such as Buginese remains challenging due to the scarcity of annotated data. This paper explores several POS tagging methods, including Unigram, Hidden Markov Model (HMM), Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU), integrated with word embedding techniques. We present a comparative analysis of these methods based on their performance on a newly collected and annotated Bugis language dataset. Our results demonstrate that advanced neural models outperform traditional statistical methods, highlighting the potential of deep learning techniques for low-resource language processing, especially RNN, which gets a higher and significant average F1 Score of 97.54%.

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