Topic Modeling 历些毛子代枚大学 XIDIAN UNIVERSITY ▣A topic ■A word cluster→a group of words Not clustered randomly,but meaningfully(not semantically) ▣Models auto car make engine emissions hidden ■Parametric models bonnet hood Markov tyres make model Latent Semantic Indexing(LSI) lorry model emissions boot trunk normalize >PLSI;Latent Dirichlet Allocation(LDA) Non-parametric models(Dirichlet Process) >(Nested)Chinese Restaurant Process Indian Buffet Process Pitman-Yor Process 2016/12/29 Software Engineering 2016/12/29 Software Engineering Topic Modeling  A topic  A word cluster  a group of words  Not clustered randomly, but meaningfully (not semantically) 8  Models  Parametric models  Latent Semantic Indexing (LSI)  PLSI; Latent Dirichlet Allocation (LDA)  Non-parametric models (Dirichlet Process)  (Nested) Chinese Restaurant Process  Indian Buffet Process  Pitman-Yor Process