Topic model
In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more do
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Algorithms for topic modelingCognitive linguisticsComputational journalismConcentration parameterDavid BleiDay of ArchaeologyDecisive Analytics CorporationDeeplearning4jDiffusion waveletsDigital humanitiesDirichlet-multinomial distributionDistributional semanticsDocument clusteringExplicit semantic analysisFranco MorettiGensimGibbs samplingGraphLabHierarchical Dirichlet processHimabindu LakkarajuImplicit authenticationLatent Dirichlet allocationLatent semantic analysisList of statistics articlesMachine learningMallet (software project)MemetrackerMixture modelOnline content analysisOutline of machine learningOutline of object recognitionPachinko allocationParticipatory rural appraisalRestricted Boltzmann machineSentiment analysisShallow parsingStochastic block modelStructured sparsity regularizationTensor rank decompositionText segmentation
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Topic model
In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more do
has abstract
En apprentissage automatique e ...... es abstraits dans un document.
@fr
In machine learning and natura ...... formatics and computer vision.
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Nell'apprendimento automatico ...... tica e la visione artificiale.
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Тематическое моделирование — с ...... мер, таких как биоинформатика.
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主题模型(Topic Model)在机器学习和自然语言处理等 ...... 语言处理相关方向,但目前以及延伸至例如生物信息学的其它领域。
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InternetArchiveBot
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date
July 2018
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fix-attempted
no
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hypernym
type
comment
En apprentissage automatique e ...... es abstraits dans un document.
@fr
In machine learning and natura ...... bably be about 9 times more do
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Nell'apprendimento automatico ...... verse; quindi, in un documento
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Тематическое моделирование — с ...... окументов и новостных потоков.
@ru
主题模型(Topic Model)在机器学习和自然语言处理等 ...... 语言处理相关方向,但目前以及延伸至例如生物信息学的其它领域。
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label
Topic model
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Topic model
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Topic model
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Тематическое моделирование
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主题模型
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토픽 모델
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