Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23525-7_27 | ECML/PKDD |
Keywords | Field | DocType |
Topic modeling,Distributed representation | Architecture,Inference,Computer science,Probabilistic analysis of algorithms,Artificial intelligence,Topic model,Deep learning,Artificial neural network,Distributed representation,Machine learning,Empirical research | Conference |
Volume | ISSN | Citations |
9285 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debakar Shamanta | 1 | 0 | 0.34 |
| Sheikh Motahar Naim | 2 | 9 | 2.97 |
| Parang Saraf | 3 | 155 | 11.98 |
| Naren Ramakrishnan | 4 | 1913 | 176.25 |
| M. Shahriar Hossain | 5 | 155 | 14.91 |