Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 441-457

Concurrent Inference of Topic Models and Distributed Vector Representations

  • Debakar Shamanta
  • Sheikh Motahar Naim
  • Parang Saraf
  • Naren Ramakrishnan
  • M. Shahriar Hossain
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)

Abstract

Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces distributed representation of topics to capture topical themes in a dataset. Unlike many state-of-the-art techniques for generating distributed representation of words and documents that directly use neighboring words for training, we leverage the outcome of a sophisticated deep neural network to estimate the topic labels of each document. The networks, for topic modeling and generation of distributed representations, are trained concurrently in a cascaded style with better runtime without sacrificing the quality of the topics. Empirical studies reported in the paper show that the distributed representations of topics represent intuitive themes using smaller dimensions than conventional topic modeling approaches.

Keywords

Topic modeling Distributed representation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Debakar Shamanta
    • 1
  • Sheikh Motahar Naim
    • 1
  • Parang Saraf
    • 2
  • Naren Ramakrishnan
    • 2
  • M. Shahriar Hossain
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA
  2. 2.Department of Computer ScienceVirginia TechArlingtonUSA

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