Topic Inference

Topic inference aims to automatically identify the underlying themes or topics within a collection of text data, a crucial task with applications ranging from authorship verification to research proposal categorization. Current research focuses on improving the robustness and efficiency of topic inference models, addressing challenges like data imbalance, topic leakage, and scalability to large vocabularies. This involves exploring various model architectures, including transformer-based approaches and spectral algorithms, and developing techniques to mitigate biases stemming from data characteristics and user behavior, ultimately leading to more accurate and fair topic identification across diverse datasets.