Online Latent Dirichlet Allocation with Infinite Vocabulary
Online Latent Dirichlet Allocation with Infinite Vocabulary Ke Zhai zhaike@cs.umd.edu Department of Computer Science, University of Maryland, College Park, MD USA Jordan Boyd-Graber jbg@umiacs.umd.edu iSchool and UMIACS, University of Maryland, College Park, MD USA Abstract Topic models based on latent Dirichlet alloca- tion (LDA) assume a predefined vocabulary. This is reasonable in batch setting
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades Xinran He1 XINRANHE@USC.EDU Theodoros Rekatsinas2 THODREK@CS.UMD.EDU James Foulds3 JFOULDS@UCSC.EDU Lise Getoor3 GETOOR@SOE.UCSC.EDU Yan Liu1 YANLIU.CS@USC.EDU 1 University of Southern California, 2 University of Maryland, College Park, 3 University of California, Santa Cruz Abstract Understanding the diff
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http://www.qucosa.de/fileadmin/data/qucosa/documents/20079/thesis_final.pdf
Chapter 9: Continuous-Time Infinite Dynamic Topic Models: The Dim Sum Process for Simultaneous Topic Enumeration and Formation
A Survey of Topic Modeling in Text Mining
