Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/2194
Title: AUTOMATIC TAG RECOMMENDATION FOR JOURNAL ABSTRACTS USING STATISTICAL TOPIC MODELING
Other Titles: EmergingICT for Bridging the Future
Authors: Anupriya P
Karpagavalli S
Keywords: Topic modeling
Latent Dirichlet Allocation
Gibbs sampling
Tag Recommendation
Issue Date: 2015
Publisher: Springer Advances in Intelligent Systems and Computing(AISC Series)
Abstract: Topic 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.
URI: https://link.springer.com/chapter/10.1007/978-3-319-13731-5_61
http://localhost:8080/xmlui/handle/123456789/2194
ISBN: Print:978-3-319-13730-8
Online:978-3-319-13731-5
Appears in Collections:International Conference

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