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 cooccurrence 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.