A comparative analysis of deep learning and machine learning for POS tagging

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3APT6ZLZ3T" target="_blank" >RIV/00216208:11320/26:PT6ZLZ3T - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.eswa.2025.128026" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2025.128026</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2025.128026" target="_blank" >10.1016/j.eswa.2025.128026</a>

Result language

angličtina

Original language name

A comparative analysis of deep learning and machine learning for POS tagging

Original language description

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. © 2025 Elsevier Ltd

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

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Volume of the periodical

288

Issue of the periodical within the volume

2025

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

128026

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105006676145