A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AU5GV2AX6" target="_blank" >RIV/00216208:11320/26:U5GV2AX6 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s12559-024-10356-3" target="_blank" >http://dx.doi.org/10.1007/s12559-024-10356-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12559-024-10356-3" target="_blank" >10.1007/s12559-024-10356-3</a>

Result language

angličtina

Original language name

A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media

Original language description

This study introduces an innovative automatic labeling framework to address the challenges of lexical normalization in social media texts for low-resource languages like Vietnamese. Social media data is rich and diverse, but the evolving and varied language used in these contexts makes manual labeling labor-intensive and expensive. To tackle these issues, we propose a framework that integrates semi-supervised learning with weak supervision techniques. This approach enhances the quality of the training dataset and expands its size while minimizing manual labeling efforts. Our framework automatically labels raw data, converting non-standard vocabulary into standardized forms, thereby improving the accuracy and consistency of the training data. Experimental results demonstrate the effectiveness of our weak supervision framework in normalizing Vietnamese text, especially when utilizing pre-trained language models. The proposed framework achieves an impressive F1-score of 82.72% and maintains vocabulary integrity with an accuracy of up to 99.22%. Additionally, it effectively handles undiacritized text under various conditions. This framework significantly enhances natural language normalization quality and improves the accuracy of various NLP tasks, leading to an average accuracy increase of 1–3%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

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

Cognitive Computation

ISSN

1866-9956

e-ISSN

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Volume of the periodical

17

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

57

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85217388148