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dc.contributor.authorAnupriya, P-
dc.contributor.authorKarpagavalli, S-
dc.date.accessioned2023-11-09T05:05:06Z-
dc.date.available2023-11-09T05:05:06Z-
dc.date.issued2015-
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-13731-5_61-
dc.description.abstractTopic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models conceive latent topics in text using hidden random variables, and discover that structure with posterior inference. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction and similarity search in the broad fields of text mining, information retrieval, statistical language modeling.In this work, a dataset with 200 abstracts fall under four topics are collected from two different domain journals for tagging journal abstracts. The document model is built using LDA (Latent Dirichlet Allocation) with Collapsed Variational Bayes (CVB0) and Gibbs sampling. Then the built model is used to find appropriate tag for a given abstract. An interface is designed to extract and recommend the tag for a given abstract.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Linken_US
dc.subjectTopic modelingen_US
dc.subjectLatent Dirichlet Allocationen_US
dc.subjectGibbs samplingen_US
dc.subjectTag Recommendationen_US
dc.titleAUTOMATIC TAG RECOMMENDATION FOR JOURNAL ABSTRACTS USING STATISTICAL TOPIC MODELINGen_US
dc.typeOtheren_US
Appears in Collections:3.Conference Paper (08)

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