Examples of using Semantic analysis in English and their translations into Russian
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Hayk Aslanyan, Artiom Baloian Scalable code clone detection tool based on semantic analysis Proceedings of the Institute for System Programming.
NMF is identical to the Probabilistic latent semantic analysis, a popular document clustering method.
Semantic analysis of the information war in politics:(on the example of the Ossetian-Ingush conflict): author's abstract.
Artiom Baloian Scalable code clone detection tool based on semantic analysis pp.
probabilistic latent semantic analysis, trained by maximum likelihood estimation.
various kinds of semantic analysis(e.g., type checks
Finally, he will outline a case study and cost advantages of using distributed computing for big data based machine learning and semantic analysis technologies.
Document-term matrix Used in latent semantic analysis, stores the occurrences of words in documents in a two-dimensional sparse matrix.
morphological analasis, semantic analysis, a pragmatic analysis. .
The study is based on two complementary methods: semantic analysis of metadata about the cities
Latent semantic analysis(LSA) is a technique in natural language processing,
bibliometrics, semantic analysis(namely, Subject Action Object analysis,
probabilistic latent semantic analysis is based on a mixture decomposition derived from a latent class model.
those that enable reliable automatic semantic analysis of the language.
it produces a systematic and semantic analysis for the correctness and appropriateness of building a sense of prepositions.
The actions of a compiler are traditionally broken up into syntax analysis(scanning and parsing), semantic analysis(determining what a program should do),
the new types of characteristics based on the text semantic analysis, heuristics, and also on plagiarism detection methods.
are used such as: Independent component analysis Isomap Kernel PCA Latent semantic analysis Partial least squares Principal component analysis Multifactor dimensionality reduction Nonlinear dimensionality reduction Multilinear Principal Component Analysis Multilinear subspace learning Semidefinite embedding Autoencoder One very important area of application is image processing, in which algorithms
Nearest neighbor search MinHash Information gain in decision trees Semidefinite embedding Multifactor dimensionality reduction Multilinear subspace learning Multilinear PCA Random projection Singular value decomposition Latent semantic analysis Semantic mapping Topological data analysis Locality sensitive hashing Sufficient dimension reduction Data transformation(statistics) Weighted correlation network analysis Hyperparameter optimization CUR matrix approximation Envelope model Nonlinear dimensionality reduction Sammon mapping Johnson-Lindenstrauss lemma Local tangent space alignment Roweis, S. T.; Saul, L. K. 2000.
There is a structural and semantic analyses of phraseological units belonging to the phraseological paradigm.