The LDA Topic Model Extension Study
Qingquan Yang, Weijiang Li
Available Online July 2015.
- https://doi.org/10.2991/lemcs-15.2015.169How to use a DOI?
- LDA; Text Retrieval; Semantics Mining; Topic Model; Document Generation
- This article is a literature review that introduces the status quo of research on the LDA in recent years. The topic model of LDA (Latent Dirichlet Allocation) was proposed by D.M. Blei in 2003, which obtained three Bayesian probability model on the extension of the probability of latent indexing (probabilistic Latent Semantic Indexing, pLSI ). It consists of the documents, topics and words, which mining implicit subject of the statistical probability model from a semantic document by modeling. In this article, researchers briefly introduce the LDA topic model and the documents generation process, but also introduce several the extended model based on LDA that has a mainstream representative part and advancement, sum up the research methods of extension of the study based on LDA, show the results in the field of study, and give personal suggestions to the future study of topic model based on LDA.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qingquan Yang AU - Weijiang Li PY - 2015/07 DA - 2015/07 TI - The LDA Topic Model Extension Study BT - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015) PB - Atlantis Press SP - 857 EP - 860 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.169 DO - https://doi.org/10.2991/lemcs-15.2015.169 ID - Yang2015/07 ER -