Topic Structure

Topic structure analysis focuses on identifying and modeling the thematic organization within textual data, particularly in dialogues and conversations. Current research emphasizes unsupervised learning methods, often employing graph neural networks and topic modeling techniques like Latent Dirichlet Allocation (LDA), to segment text into topically coherent units and understand the relationships between these segments. This work is crucial for improving the performance of dialogue systems, enabling more natural and coherent interactions, and providing valuable insights into the dynamics of human communication across various platforms like Twitter.