Summary of A Weakly Supervised Data Labeling Framework For Machine Lexical Normalization in Vietnamese Social Media, by Dung Ha Nguyen et al.
A Weakly Supervised Data Labeling Framework for Machine Lexical Normalization in Vietnamese Social Media
by Dung Ha Nguyen, Anh Thi Hoang Nguyen, Kiet Van Nguyen
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework integrates semi-supervised learning with weak supervision techniques to address the challenges of lexical normalization in social media texts for low-resource languages like Vietnamese. The approach enhances the quality and size of the training dataset while minimizing manual labeling efforts, automatically converting non-standard vocabulary into standardized forms. Experimental results demonstrate the effectiveness of the framework in normalizing Vietnamese text, achieving an impressive F1-score of 82.72% and maintaining vocabulary integrity with an accuracy of up to 99.22%. The framework can handle undiacritized text under various conditions, significantly enhancing natural language normalization quality and improving the accuracy of various NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to make computers understand social media texts in languages that don’t have many resources available. This is important because social media data is very diverse and evolving quickly, making it hard for humans to label and prepare it for use in machines. The new method uses a combination of machine learning and weak supervision techniques to automatically convert non-standard vocabulary into standardized forms. This makes the training data more accurate and consistent. The researchers tested their method on Vietnamese text and found that it worked well, achieving an impressive accuracy rate. This breakthrough has the potential to improve the accuracy of various natural language processing tasks. |
Keywords
» Artificial intelligence » F1 score » Machine learning » Natural language processing » Nlp » Semi supervised
