Sentence examples for using the latent semantic from inspiring English sources

Specifically, using the latent semantic analysis techniques, the AutoTL first extracts the semantic-related keywords as the seed feature set between the online web (long text) data and the target data.

More recently, regarding the automatic evaluation of written answers, there are some promising approaches that use the latent semantic analysis (LSA) model [4, 8, 10 12].

Text matrix is used in the latent semantic analysis.

Hence, we utilize the latent semantic analysis approach instead [28, 29].

The final classifier is an LS-SVM array with Gaussian radial basis function (GRBF) kernel, which uses the coefficients generated by the latent semantic indexing algorithm for classification of the text titles.

Then the EM method [28, 29] is used to analyze the relationship and extract the latent semantic concepts.

Then the generative LDA model is used to project the original feature space into the latent semantic space to obtain low dimensional latent topic features.

The AutoTL adopts the latent semantic analysis to dig the semantics of both the target domain and the source domain.

For query semantic extension, latent semantic analysis is adopted to discover the latent semantic relationships between queries and ontology semantic features, and ontology semantic graph is used to represent the query.

In this paper, we apply Non-negative Sparse Latent Semantic Analysis (NN-Sparse LSA) to discover the latent semantic space behind associations between multimedia resources and tagging data.

The Latent Semantic Index low-dimensional space is also called semantic space.