This HTML5 document contains 48 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
yago-reshttp://yago-knowledge.org/resource/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n15https://global.dbpedia.org/id/
yagohttp://dbpedia.org/class/yago/
dbthttp://dbpedia.org/resource/Template:
rdfshttp://www.w3.org/2000/01/rdf-schema#
freebasehttp://rdf.freebase.com/ns/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
dbphttp://dbpedia.org/property/
dbchttp://dbpedia.org/resource/Category:
provhttp://www.w3.org/ns/prov#
xsdhhttp://www.w3.org/2001/XMLSchema#
goldhttp://purl.org/linguistics/gold/
wikidatahttp://www.wikidata.org/entity/
dbrhttp://dbpedia.org/resource/

Statements

Subject Item
dbr:Dynamic_topic_model
rdf:type
yago:YagoLegalActor yago:YagoLegalActorGeo yago:Assistant109815790 yago:Person100007846 yago:Whole100003553 yago:Object100002684 yago:Worker109632518 yago:PhysicalEntity100001930 yago:WikicatLatentVariableModels yago:Organism100004475 yago:Model110324560 yago:LivingThing100004258 dbo:Person yago:CausalAgent100007347
rdfs:label
Dynamic topic model
rdfs:comment
Within statistics, Dynamic topic models' are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents.
dcterms:subject
dbc:Statistical_natural_language_processing dbc:Latent_variable_models
dbo:wikiPageID
34073580
dbo:wikiPageRevisionID
1073994495
dbo:wikiPageWikiLink
dbr:PLSA dbr:Latent_Dirichlet_Allocation dbr:Latent_Dirichlet_allocation dbr:Generative_model dbr:Statistics dbr:Multinomial_distribution dbr:Exponential_family dbr:De_Finetti's_theorem dbr:Variational_methods dbc:Latent_variable_models dbr:Gibbs_sampling dbc:Statistical_natural_language_processing dbr:David_Blei
owl:sameAs
freebase:m.0hq_3dw n15:4j8Vu yago-res:Dynamic_topic_model wikidata:Q5319030
dbp:wikiPageUsesTemplate
dbt:Reflist dbt:Math
dbo:abstract
Within statistics, Dynamic topic models' are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents. In LDA, both the order the words appear in a document and the order the documents appear in the corpus are oblivious to the model. Whereas words are still assumed to be exchangeable, in a dynamic topic model the order of the documents plays a fundamental role. More precisely, the documents are grouped by time slice (e.g.: years) and it is assumed that the documents of each group come from a set of topics that evolved from the set of the previous slice.
gold:hypernym
dbr:Models
prov:wasDerivedFrom
wikipedia-en:Dynamic_topic_model?oldid=1073994495&ns=0
dbo:wikiPageLength
5815
foaf:isPrimaryTopicOf
wikipedia-en:Dynamic_topic_model
Subject Item
dbr:John_D._Lafferty
dbo:wikiPageWikiLink
dbr:Dynamic_topic_model
Subject Item
dbr:List_of_statistics_articles
dbo:wikiPageWikiLink
dbr:Dynamic_topic_model
Subject Item
dbr:Outline_of_machine_learning
dbo:wikiPageWikiLink
dbr:Dynamic_topic_model
Subject Item
wikipedia-en:Dynamic_topic_model
foaf:primaryTopic
dbr:Dynamic_topic_model