mlsanityData to 7 Oct 2026

ViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization

arXiv 2501.07020 ↗First posted 13 Jan 2025cs.CL cs.AIcited by 2

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Abstract (arXiv)

ViSoLex is an open-source system designed to address the unique challenges of lexical normalization for Vietnamese social media text. The platform provides two core services: Non-Standard Word (NSW) Lookup and Lexical Normalization, enabling users to retrieve standard forms of informal language and standardize text containing NSWs. ViSoLex's architecture integrates pre-trained language models and weakly supervised learning techniques to ensure accurate and efficient normalization, overcoming the scarcity of labeled data in Vietnamese. This paper details the system's design, functionality, and its applications for researchers and non-technical users. Additionally, ViSoLex offers a flexible, customizable framework that can be adapted to various datasets and research requirements. By publishing the source code, ViSoLex aims to contribute to the development of more robust Vietnamese natural language processing tools and encourage further research in lexical normalization. Future directions include expanding the system's capabilities for additional languages and improving the handling of more complex non-standard linguistic patterns.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 13 Jan 2025.

Builds on

5 of 11 references.

References · 5

  1. 2021Self-Training with Weak Supervisioncited by 49
  2. 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 33
  3. 2023ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processingcited by 11
  4. 2021VSEC: Transformer-based Model for Vietnamese Spelling Correctioncited by 6
  5. 2024A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Mediacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.