Summary of Multilingual Transformer and Bertopic For Short Text Topic Modeling: the Case Of Serbian, by Darija Medvecki et al.
Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian
by Darija Medvecki, Bojana Bašaragin, Adela Ljajić, Nikola Milošević
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper explores the application of BERTopic, a cutting-edge topic modeling technique, to short texts in Serbian, a morphologically rich language. The authors evaluate BERTopic’s performance on partially preprocessed tweets expressing hesitancy towards COVID-19 vaccination, comparing it to LDA and NMF on fully preprocessed text. They find that with proper parameter settings, BERTopic can yield informative topics even when applied to partial text preprocessing, with minimal performance drops compared to full preprocessing. The study shows that BERTopic offers more informative topics and novel insights, especially when the number of topics is not limited. This research has implications for researchers working with other morphologically rich low-resource languages and short texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine using a powerful tool to understand what people are talking about in short messages, like tweets. Researchers used this tool, called BERTopic, to analyze Serbian language tweets that express doubt about getting vaccinated against COVID-19. They tested it on two types of text: fully processed and partially processed. The results show that BERTopic can work well even with incomplete text! It gives more accurate topics and new insights compared to other methods. This discovery is important for people who study languages and want to understand what’s being said in different parts of the world. |
