Use Context Information to Improve the Performance of LatentDirichlet Allocation
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === Latent Dirichlet Allocation (LDA), is a wildly used topic model for discovering the topics in documents, however it suffers from many problems like lack of dependency between words and sparse data. The main cause of these problems is the word-sense disambigua...
Main Authors: | Che-Yi Lin, 林哲毅 |
---|---|
Other Authors: | 鄭卜壬 |
Format: | Others |
Language: | en_US |
Published: |
2014
|
Online Access: | http://ndltd.ncl.edu.tw/handle/24862892672850460435 |
Similar Items
-
Latent Dirichlet Allocation in R
by: Ponweiser, Martin
Published: (2012) -
Multi-dependent Latent Dirichlet Allocation
by: Wei-ChengHsin, et al.
Published: (2016) -
Text Categorization with Latent Dirichlet Allocation
by: ZLACKÝ Daniel, et al.
Published: (2014-05-01) -
Latent Dirichlet Allocation、classification、feature scaling、positive and negative data
by: Shih-Yi Kuo, et al.
Published: (2011) -
Tag recommendation using Latent Dirichlet Allocation.
by: Choubey, Rahul
Published: (2011)