Transversal Text Mining Techniques
Nicolas Turenne, Jean‐Charles Pomerol · 2013
Levels of analysis of the relations can benefit generic methods, particularly on aspects of matrix analysis or statistical learning analysis. Such is the case of latent semantic indexing and approaches to extraction of named entities. “Inverse methods” provide invaluable mathematical tools which allow us to work back from observations to models. Latent semantic analysis (LSA) is a statistical technique intended to estimate the hidden structure of content in documents. Finally, LSA is a technique which is popular in text mining, but which yields the same results are main component analysis. From a general point of view, an ontology serves to uniformize (standardize) the use of a language, or at least disambiguate a vocabulary, and therefore must favor communication between the actors of a project. Nonlinear regression algorithms, just like feed-forward artificial neural networks, adaptive spline methods and continued projection methods are used to carry out classifications and clustering.