用预训练模型+聚类，自动发现更连贯的文本主题。
Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms
- 用Transformer生成文档嵌入，捕捉上下文语义
- 降维后聚类，每个簇生成一个连贯主题
- 比ChatGPT和传统方法更准确，适合文本分析
主题建模是一种在无先验知识的情况下发现文档集合中隐藏主题和模式的强大技术。传统主题建模与基于聚类的方法难以捕捉上下文语义信息。本研究提出一种创新的端到端语义驱动主题建模方法，利用先进的词与文档嵌入及强大聚类算法进行主题提取。该方法通过预训练的Transformer语言模型生成文档嵌入，对嵌入进行降维，基于语义相似性进行聚类，并为每个聚类生成连贯的主题。相比ChatGPT和传统主题建模算法，该模型能生成更连贯、更有意义的主题。
原文摘要 · Abstract (English)
Topic modeling is a powerful technique to discover hidden topics and patterns within a collection of documents without prior knowledge. Traditional topic modeling and clustering-based techniques encounter challenges in capturing contextual semantic information. This study introduces an innovative end-to-end semantic-driven topic modeling technique for the topic extraction process, utilizing advanced word and document embeddings combined with a powerful clustering algorithm. This semantic-driven approach represents a significant advancement in topic modeling methodologies. It leverages contextual semantic information to extract coherent and meaningful topics. Specifically, our model generates document embeddings using pre-trained transformer-based language models, reduces the dimensions of the embeddings, clusters the embeddings based on semantic similarity, and generates coherent topics for each cluster. Compared to ChatGPT and traditional topic modeling algorithms, our model provides more coherent and meaningful topics.
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