会议专题

LATENT SEMANTIC ANALYSIS BASED ON SPACE INTEGRATION

  Latent Semantic Analysis (LSA) is a technology which is used to analyze the latent concepts.LSA is based on the Vector Space Model (VSM) and statistics,and it usually takes the Singular Value Decomposition (SVD) as the kernel algorithm.Always,LSA increases the scale of the training data to improve system performance.However,as it needs many extra operations,and it also generates too much co-occurrence paths which are unreasonable between the different features,the problem of noise will be a serious disadvantage.This paper proposes a new method which is called augmented space model to optimize the latent semantic space model.Besides,it is also suggested in this paper that multiple models can be combined with integration technology to improve system performance.Through integration technology and space optimization,the models may describe the latent semantic structure more exactly.At the same time,to some extent,the probability of generating noise co-occurrence is reduced.As shown from comparative experiments,the system accuracy is higher after adopting integration technology and space optimization technology.

Latent semantic analysis Augmented space model Space integration Space optimization Text categorization K-nearest neighbor

Dongfeng Cai Liwei Chang Duo Ji

Knowledge Engineering Research Center,Shen Yang Aerospace University,Shenyang 110136,China

国际会议

2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

杭州

英文

1912-1916

2012-10-30(万方平台首次上网日期,不代表论文的发表时间)