Skip to main content

Learning topic models -- provably and efficiently

Author(s): Arora, Sanjeev; Ge, Rong; Halpern, Yoni; Mimno, David; Moitra, Ankur; et al

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1rn97
Full metadata record
DC FieldValueLanguage
dc.contributor.authorArora, Sanjeev-
dc.contributor.authorGe, Rong-
dc.contributor.authorHalpern, Yoni-
dc.contributor.authorMimno, David-
dc.contributor.authorMoitra, Ankur-
dc.contributor.authorSontag, David-
dc.contributor.authorWu, Yichen-
dc.contributor.authorZhu, Michael-
dc.date.accessioned2021-10-08T19:49:23Z-
dc.date.available2021-10-08T19:49:23Z-
dc.date.issued2018en_US
dc.identifier.citationArora, Sanjeev, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. "Learning topic models--provably and efficiently." Communications of the ACM 61, no. 4 (2018): pp. 85-93. doi:10.1145/3186262en_US
dc.identifier.issn0001-0782-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1rn97-
dc.description.abstractToday, we have both the blessing and the curse of being over- loaded with information. Never before has text been more important to how we communicate, or more easily avail- able. But massive text streams far outstrip anyone’s ability to read. We need automated tools that can help make sense of their thematic structure, and find strands of meaning that connect documents, all without human supervision. Such methods can also help us organize and navigate large text corpora. Popular tools for this task range from Latent Semantic Analysis (LSA)8 which uses standard linear algebra, to deep learning which relies on non-convex optimization. This paper concerns topic modeling which posits a simple probabilistic model of how a document is generated. We give a formal description of the generative model at the end of the section, but next we will outline its important features.en_US
dc.format.extent85 - 93en_US
dc.language.isoen_USen_US
dc.relation.ispartofCommunications of the ACMen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleLearning topic models -- provably and efficientlyen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1145/3186262-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

Files in This Item:
File Description SizeFormat 
LearningTopicModels.pdf3.56 MBAdobe PDFView/Download


Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.