A comparative analysis of deep learning and machine learning for POS tagging. Baruah, N. & Goutom, P. J. Expert Systems with Applications, 288:128026, 2025. ![A comparative analysis of deep learning and machine learning for POS tagging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Natural Language Processing (NLP) has experienced substantial alteration with the emergence of deep learning (DL), which increasingly prefers end-to-end architectures over conventional pipeline methodologies (for example, tokenization, POS tagging, and parsing). While Part-of-Speech (POS) labeling was formerly essential for syntactic parsing and downstream activities such as early machine translation, newer systems often avoid explicit POS annotation. Nonetheless, POS tagging remains relevant in particular contexts: (1) facilitating syntactic analysis in hybrid NLP systems, (2) providing linguistic scaffolding for low-resource languages where data-hungry DL models fail, and (3) enhancing interpretability in grammatical annotation tasks. This article gives a thorough assessment of POS tagging strategies from 2019 to 2023, including rule-based, statistical, machine learning, and deep learning techniques. We investigate their technological growth, capabilities, and limits, with a focus on their incorporation into modern NLP processes. Our results highlight new trends, such as parameter-efficient tagging for multilingual situations, as well as ongoing issues in morphologically rich languages. This paper highlights the transitory function of POS tagging within current NLP paradigms, which is lessened in general-purpose applications but persists in specialized areas, and reveals synergies between modular language analysis and end-to-end systems.
@article{baruah_comparative_2025,
title = {A comparative analysis of deep learning and machine learning for {POS} tagging},
volume = {288},
issn = {0957-4174},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425016471},
doi = {https://doi.org/10.1016/j.eswa.2025.128026},
abstract = {Natural Language Processing (NLP) has experienced substantial alteration with the emergence of deep learning (DL), which increasingly prefers end-to-end architectures over conventional pipeline methodologies (for example, tokenization, POS tagging, and parsing). While Part-of-Speech (POS) labeling was formerly essential for syntactic parsing and downstream activities such as early machine translation, newer systems often avoid explicit POS annotation. Nonetheless, POS tagging remains relevant in particular contexts: (1) facilitating syntactic analysis in hybrid NLP systems, (2) providing linguistic scaffolding for low-resource languages where data-hungry DL models fail, and (3) enhancing interpretability in grammatical annotation tasks. This article gives a thorough assessment of POS tagging strategies from 2019 to 2023, including rule-based, statistical, machine learning, and deep learning techniques. We investigate their technological growth, capabilities, and limits, with a focus on their incorporation into modern NLP processes. Our results highlight new trends, such as parameter-efficient tagging for multilingual situations, as well as ongoing issues in morphologically rich languages. This paper highlights the transitory function of POS tagging within current NLP paradigms, which is lessened in general-purpose applications but persists in specialized areas, and reveals synergies between modular language analysis and end-to-end systems.},
journal = {Expert Systems with Applications},
author = {Baruah, Nomi and Goutom, Pritom Jyoti},
year = {2025},
keywords = {Deep learning, Machine learning, NLP, POS Tagging},
pages = {128026},
}
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Nonetheless, POS tagging remains relevant in particular contexts: (1) facilitating syntactic analysis in hybrid NLP systems, (2) providing linguistic scaffolding for low-resource languages where data-hungry DL models fail, and (3) enhancing interpretability in grammatical annotation tasks. This article gives a thorough assessment of POS tagging strategies from 2019 to 2023, including rule-based, statistical, machine learning, and deep learning techniques. We investigate their technological growth, capabilities, and limits, with a focus on their incorporation into modern NLP processes. Our results highlight new trends, such as parameter-efficient tagging for multilingual situations, as well as ongoing issues in morphologically rich languages. 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