Probabilistic Topic

Probabilistic topic modeling aims to uncover latent thematic structures within collections of text data, facilitating text summarization, categorization, and exploration. Current research focuses on integrating advancements in deep learning, particularly transformer-based embeddings and neural network architectures like variational autoencoders, to improve topic coherence and address challenges like topic granularity and hallucination in large language model-based approaches. These improvements enhance the interpretability and accuracy of topic models, impacting fields like insurance risk assessment, qualitative research analysis, and cross-lingual information retrieval.