Block Bayesian Sparse Topical Coding
Paper
Paper/Presentation Title | Block Bayesian Sparse Topical Coding |
---|---|
Presentation Type | Paper |
Authors | Peng, Min (Author), Shi, Hongliang (Author), Xie, Qianqian (Author), Zhang, Yihan (Author), Wang, Hua (Author), Li, Zhaoyunfei (Author) and Yong, Jianming (Author) |
Journal or Proceedings Title | Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design |
ERA Conference ID | 43280 |
Number of Pages | 6 |
Year | 2018 |
Place of Publication | United States |
ISBN | 9781538614822 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CSCWD.2018.8465366 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8465366 |
Conference/Event | 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design |
International Conference on Computer Supported Cooperative Work in Design | |
Event Details | International Conference on Computer Supported Cooperative Work in Design CSCWD Rank B B B B B B B B B |
Event Details | 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design Event Date 09 to end of 11 May 2018 Event Location Nanjing, China |
Abstract | Learning low dimensional representations from a large number of short corpora has a profound practical significance but with vital challenge in content analysis and data mining applications. In this paper, we propose a novel topic model called Block Bayesian Sparse Topic Coding (Block-BSTC), which is capable of discovering the latent semantic representation of short texts. The Block-BSTC relaxes the normalization constraint of the inferred representations with word embeddings and block sparse Bayesian learning, which is convenient to directly control the sparsity of word codes with exploiting the intra-block correlations. Furthermore, the experimental results show that Block-BSTC achieves great performance on the sparsity ratio of word codes. Meanwhile, it can improve the accuracy of document classification. |
Keywords | Sparse Topical Coding, Block Bayesian Sparse Learning, word embeddings |
ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University, China |
National University of Singapore | |
Victoria University | |
Jinan University, China | |
School of Management and Enterprise | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q50w5/block-bayesian-sparse-topical-coding
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