Topic Embeddings

Topic embeddings represent textual data, such as words or documents, as vectors in a continuous space, aiming to capture semantic relationships and facilitate various downstream tasks like topic modeling and stance detection. Current research focuses on improving the quality and generalizability of these embeddings, exploring techniques like contrastive learning, box embeddings, and incorporating user interaction to refine topic representations within neural topic models (NTMs) and other architectures. This work is significant because improved topic embeddings enhance the performance of numerous natural language processing applications, including information retrieval, summarization, and social media analysis, leading to more accurate and insightful results.