多语言Transformer与BERTopic用于短文本主题建模：以塞尔维亚语为例
Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian
AI总结：
本文首次将BERTopic应用于塞尔维亚语短文本主题建模，验证其在部分预处理下仍能生成信息丰富的主题，并优于LDA和NMF，为形态丰富低资源语言研究提供参考。
摘要：
本文展示了首次将最先进的主题建模技术BERTopic应用于形态丰富语言短文本的结果。我们使用三种多语言嵌入模型，在两种文本预处理水平（部分预处理和完全预处理）下应用BERTopic，以评估其在塞尔维亚语部分预处理短文本上的性能。我们还在完全预处理文本上将其与LDA和NMF进行了比较。实验在表达对COVID-19疫苗接种犹豫的推文数据集上进行。我们的结果表明，在适当的参数设置下，即使应用于部分预处理的短文本，BERTopic也能产生信息丰富的主题。当在两种预处理场景中应用相同参数时，部分预处理文本上的性能下降最小。与LDA和NMF相比，根据关键词判断，BERTopic提供了更多信息丰富的主题，并在主题数量不受限制时提供了新颖的见解。本文的发现对于研究其他形态丰富且资源匮乏的语言和短文本的研究人员具有重要意义。
英文摘要：
This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance on partially preprocessed short text in Serbian. We also compared it to LDA and NMF on fully preprocessed text. The experiments were conducted on a dataset of tweets expressing hesitancy toward COVID-19 vaccination. Our results show that with adequate parameter setting, BERTopic can yield informative topics even when applied to partially pre-processed short text. When the same parameters are applied in both prepro-cessing scenarios, the performance drop on partially preprocessed text is minimal. Compared to LDA and NMF, judging by the keywords, BERTopic offers more informative topics and gives novel insights when the number of topics is not limited. The findings of this paper can be significant for re-searchers working with other morphologically rich low-resource languages and short text.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
