Sciweavers

PKDD
2015
Springer

Concurrent Inference of Topic Models and Distributed Vector Representations

7 years 11 months ago
Concurrent Inference of Topic Models and Distributed Vector Representations
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 in...
Debakar Shamanta, Sheikh Motahar Naim, Parang Sara
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where PKDD
Authors Debakar Shamanta, Sheikh Motahar Naim, Parang Saraf, Naren Ramakrishnan, M. Shahriar Hossain
Comments (0)