Learning Deep Topics of Interest

G. S. Mahalakshmi (), S. Hemadharsana, G. Muthuselvi and S. Sendhilkumar
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G. S. Mahalakshmi: Anna University, Department of Computer Science and Engineering
S. Hemadharsana: Anna University, Department of Computer Science and Engineering
G. Muthuselvi: Anna University, Department of Computer Science and Engineering
S. Sendhilkumar: Anna University, Department of Information Science and Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1517-1532 from Springer

Abstract: Abstract Topic Models are representations of the given text. Topic models are unsupervised in nature because they totally depend on word distributions. Few generative topic models obtain the topics proportionate to the richness of the given text. Applying deep learning for identifying the worthy distributions in a rich text is quite helpful for generation of quality topics. This paper discusses the idea of learning deep topics of interest from scientific research articles. The learning is handled using deep stacked auto-encoder with three hidden layer stack coupled with generative topic models. The deep learning framework is explored excluding and including back-propagation and is tested upon both LDA and HDP as foundational topic model. Experiments conducted over the data-set of research articles from top bio-medical journals reveal better topic coherence for learning deep topics.

Keywords: Deep learning; Stacked auto-encoder; HDP; Deep topic models (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_156

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DOI: 10.1007/978-3-030-41862-5_156

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