Modeling Science: Dynamic Topic Models of Scholarly...

Google Tech Talks May 24, 2007 ABSTRACT A surge of recent research in machine learning and statistics has developed new techniques for finding patterns of words in document collections using hierarchical probabilistic models. These models are called "topic models" because the word patterns often reflect the underlying topics that are combined to form the documents; however topic models also naturally apply to such data as images and biological sequences. While previous topic models have assumed that the corpus is static, many document collections actually change over time: scientific articles, emails, and search queries reflect evolving content, and it is important to model the corresponding...