Semantic Context-Dependent Weighting for Vector Space Model

In this paper, we represent a dynamic context-dependent weighting method for vector space model. A meaning is relatively decided by a context dynamically. A vector space model, including latent semantic indexing (LSI), etc. relatively measures correlations of each target thing that represents in each vector. However, the vectors of each target thing in almost method of the vector space models are static. It is important to weight each element of each vector by a context. Recently, it is necessary to understand a certain thing by not reading one data but summarizing massive data. Therefore, the vectors in the vector space model create from data set corresponding to represent a certain thing. That is, we should create vectors for the vector space model dynamically corresponding to a context and data distribution. The features of our method are a dynamic calculation of each element of vectors in a vector space model corresponding to a context. Our method reduces a vector dimension corresponding to context by context-depending weighting. Therefore, We can measure correlation with low calculation cost corresponding to context because of dimension deduction.