Comparison of Stochastic and Rule-Based POS Tagging on Malay Online Text

Anbananthen, Kalaiarasi Sonai Muthu; Krishnan, Jaya Kumar; Sayeed, Mohd. Shohel; Muniapan, Praviny

doi:10.3844/ajassp.2017.843.851

Cited by 13 publications

(2 citation statements)

References 10 publications

(10 reference statements)

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“…Various methods have been proposed for aspect extraction, including rule-based, unsupervised, and supervised methods. Rule-based methods use handcrafted or mined rules to identify aspects [8], while unsupervised methods rely on statistical techniques to identify aspects without needing labelled data. Supervised methods, on the other hand, use labelled data to train machine learning models to identify aspects.…”

Section: Background Studymentioning

confidence: 99%

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

Busst,

Anbananthen,

Kannan

et al. 2024

IEEE Access

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Aspect extraction poses a significant challenge in Natural Language Processing (NLP). Despite significant research efforts, extracting explicit and implicit aspects from online text data remains an ongoing challenge. Enhancing the accuracy and effectiveness of aspect extraction is an important area for improvement. This research introduces Ensemble BiLSTM, a novel approach to aspect extraction that addresses these challenges. Ensemble BiLSTM leverages the combination of the syntactic, semantic, and contextual properties of unstructured texts that are present in BERT word embeddings, along with their sequential properties that are captured using an ensemble of Bidirectional Long Short-Term Memory (BiLSTM) models. The proposed Ensemble BiLSTM model was evaluated extensively using the SemEval-2014 Restaurant, SemEval-2015 Restaurant, SemEval-2016 Laptop, and Financial Opinion Mining and Question Answering (FiQA) datasets. The experimental results demonstrate its efficacy in extracting aspects from text, achieving 91.28%, 87.39%, 95.85%, and 94.59% accuracy on the respective datasets. These promising results highlight the effectiveness of the ensemble approach and the incorporation of sequential models combined with BERT embeddings. The contributions of this research lie in the aspect category features extracted by the proposed Ensemble BiLSTM model, which can be expanded upon to generate accurate aspect-level sentiment features.

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Section: Background Studymentioning

confidence: 99%

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

Busst,

Anbananthen,

Kannan

et al. 2024

IEEE Access

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“…Part-of-speech (POS) tagging is a crucial research field under the umbrella of natural language processing (NLP). POS tagging involves assigning each word in a sentence with its corresponding part of speech tag, such as a noun, verb, or adjective [1,2]. Developing an accurate model for POS tagging requires substantial linguistic expertise and a vast amount of annotated corpora.…”

Section: Introductionmentioning

confidence: 99%

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

Adebayo,

Anbananthen,

Muthaiyah

et al. 2024

HighTech. Innov. J.

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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

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Bengali POS Tagging Using Bi-LSTM with Word Embedding and Character-Level Embedding

Bose

Sarkar

2022

Lecture Notes in Networks and Systems

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Comparison of Stochastic and Rule-Based POS Tagging on Malay Online Text

Cited by 13 publications

References 10 publications

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

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

Bengali POS Tagging Using Bi-LSTM with Word Embedding and Character-Level Embedding

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