A Comparative Study: Leveraging BERT and K-fold Cross Validation for UPOS Tagging in Bengali

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AYA9DXDBM" target="_blank" >RIV/00216208:11320/26:YA9DXDBM - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NCIM65934.2025.11159997" target="_blank" >http://dx.doi.org/10.1109/NCIM65934.2025.11159997</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NCIM65934.2025.11159997" target="_blank" >10.1109/NCIM65934.2025.11159997</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparative Study: Leveraging BERT and K-fold Cross Validation for UPOS Tagging in Bengali

Popis výsledku v původním jazyce

Universal Dependency (UD) infrastructure-based Part-of-Speech tagging also called UPOS tagging, holds critical importance for the development of higher-level Natural Language Processing (NLP) applications in Bengali. Due to its limited resources and lack of exploration, this study represents a manually created Bengali UPOS dataset and a comparative analysis utilizing recurrent-based deep learning models and transformer-based techniques to enhance tagging reliability. For the creation of the dataset, the information was gathered from news, tales, Wikipedia, and various subject areas in order to preserve the Bengali language's prevalence, and we have painstakingly compiled over 2200 sentences with more than 32000 tokens into a treebank. In order to increase UPOS tagging accuracy, we adopt a transformer-based technique with multilingual BERT (mBERT) for our dataset and initially yield an 84% accuracy after fine-tuning it. Our BERT model shows a significant performance gain when k-fold cross-validation (k=5) is applied, and the accuracy dramatically improves to 92.65% on average. We also compare our approach against other recurrent-based models (e.g., LSTM, BiLSTM) that are also applied to the same dataset, and the efficacy of a transformer-based model named BERT for morphologically rich and low-resource languages like Bengali is demonstrated. The enhanced accuracy and vast dataset in Bengali UPOS tagging could significantly impact NLP applications like dependency parsing, grammar checkers, and machine translation enhancement. © 2025 IEEE.

Název v anglickém jazyce

A Comparative Study: Leveraging BERT and K-fold Cross Validation for UPOS Tagging in Bengali

Popis výsledku anglicky

Universal Dependency (UD) infrastructure-based Part-of-Speech tagging also called UPOS tagging, holds critical importance for the development of higher-level Natural Language Processing (NLP) applications in Bengali. Due to its limited resources and lack of exploration, this study represents a manually created Bengali UPOS dataset and a comparative analysis utilizing recurrent-based deep learning models and transformer-based techniques to enhance tagging reliability. For the creation of the dataset, the information was gathered from news, tales, Wikipedia, and various subject areas in order to preserve the Bengali language's prevalence, and we have painstakingly compiled over 2200 sentences with more than 32000 tokens into a treebank. In order to increase UPOS tagging accuracy, we adopt a transformer-based technique with multilingual BERT (mBERT) for our dataset and initially yield an 84% accuracy after fine-tuning it. Our BERT model shows a significant performance gain when k-fold cross-validation (k=5) is applied, and the accuracy dramatically improves to 92.65% on average. We also compare our approach against other recurrent-based models (e.g., LSTM, BiLSTM) that are also applied to the same dataset, and the efficacy of a transformer-based model named BERT for morphologically rich and low-resource languages like Bengali is demonstrated. The enhanced accuracy and vast dataset in Bengali UPOS tagging could significantly impact NLP applications like dependency parsing, grammar checkers, and machine translation enhancement. © 2025 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Int. Conf. Next-Gener. Comput., IoT Mach. Learn., NCIM

ISBN

979-8-3315-5542-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Gazipur

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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