Applications Of Latent Dirichlet Allocation And Hierarchical Dirichlet Processes
Title: Applications of Latent Dirichlet Allocation and Hierarchical Dirichlet Processes
Abstract : Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Processes (HDP) have become popular models for discovering latent semantics from text corpora. I will first start this talk with a brief explanation of what LDA and HDP are and how they are used in common text analysis tasks. Then, I will describe our recent research that extends LDA and HDP to analyze two different text corpora: online reviews and conference proceedings. With the online reviews, we propose a variant of LDA called Aspect and Sentiment Unification Model (ASUM) to analyze topics and sentiments jointly. With the conference proceedings, we propose a variant of HDP called distant dependent Chinese Restaurant Franchise (ddCRF) to discover how new topics emerge through time. Unlike the HDP, the ddCRF makes no assumption of the exchangeability of data, and hence the model can capture relationships among data such as temporal patterns of topics.
Bio: Alice Oh is an Assistant Professor of Computer Science at Korea Advanced Institute of Science and Technology. She leads her research group, Users and Information Lab, with the vision of delivering information to satisfy the user. To that end, she studies and employs methods from machine learning, human-computer interaction, and statistical natural language processing. Alice completed her M.S. in Language and Information Technologies at CMU and her Ph.D. in Computer Science at MIT.