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Samples in periodicals archive:
Latent Semantic Analysis (LSA) is an intelligent information retrieval technique that uses mathematical algorithms for analyzing large corpora of text and revealing the underlying semantic information of documents.
They then examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization can deal with nuances of the collection process.
Among their topics are portable text summarization, text-to-text similarity of sentences, applying natural language processing to students' self-explanations, computer-aided rhetorical analysis, the morphological analysis of ill-formed Arabic verbs for second-language learners, using event semantics for toponym disambiguation, and evaluating narrative and expository text summaries using latent semantic analysis.
Established among the one hundred unique claims are methods for mathematically representing words as a function of context, and using this representative latent semantic analysis to learn from content sources separate from any searchable set.
The topics include profiling user color perception for image retrieval, an image retrieval model combining ontology and probabilistic ranking, extracting sport video semantics, a dynamic semantic and complex relationship modeling of multimedia data, and latent semantic analysis for text mining and beyond.
2 platform is built on the company's patented Probabilistic Latent Semantic Analysis (PLSA) algorithm, which powers complex searches with precision to deliver highly accurate, complete results.
A related approach (not used here) that also aims at extracting latent semantics from text documents is Probabilistic Latent Semantic Analysis (PLSA) introduced in .
Abstract This paper adapts latent semantic analysis (LSA) as an automated, statistically reliable metric for comparing desired outcomes or objectives across educational programs.