Neural Topic Model

Neural topic models (NTMs) are computational methods that leverage neural networks to discover latent topics within collections of text data, aiming to improve upon the limitations of traditional statistical approaches like LDA. Current research focuses on enhancing NTMs through integration with pre-trained language models, incorporating multimodal data (text and images), and developing more effective evaluation metrics beyond traditional coherence measures, often employing architectures like variational autoencoders and incorporating techniques like optimal transport and diffusion models. These advancements are significant because they enable more accurate, interpretable, and efficient topic discovery across diverse data types, with applications ranging from content analysis and historical research to multimodal emotion detection and improved information retrieval.