Abstract: Methods for analyzing elements of a first text collection, and devices and methods for searching for information in a set of digital data, in particular in a computer network.

Abstract: A computer-implemented method for training an artificial neural network using training data, which include features and identifiers, the features characterizing term candidates from a corpus, the corpus including a text from a domain, the identifier characterizing a degree of association to at least three classes for term candidates that differ from one another, different classes indicating different degrees of association of the term candidates to the domain, the training data including an assignment of features to identifiers. An artificial neural network, method for classifying term candidates, and computer-implemented method for generating training data, are also described.

Abstract: Methods for analyzing elements of a first text collection, and devices and methods for searching for information in a set of digital data, in particular in a computer network.

Abstract: A computer-implemented method for training an artificial neural network using training data, which include features and identifiers, the features characterizing term candidates from a corpus, the corpus including a text from a domain, the identifier characterizing a degree of association to at least three classes for term candidates that differ from one another, different classes indicating different degrees of association of the term candidates to the domain, the training data including an assignment of features to identifiers. An artificial neural network, method for classifying term candidates, and computer-implemented method for generating training data, are also described.

Abstract: A method for training a model, a classification system for voice or text classification, a method for controlling, and a assistance system. General-language word vectors and technical-language word vectors, and training data which include terms, are provided. A label is assigned to each of the terms, which indicates a specificity of the term with respect to a specialist field. A first word vector is determined as a function of the general-language word vectors and a second word vector is determined as a function of the technical-language word vectors, for a term from the training data. The model predicts a specificity of the term with respect to the specialist field as a function of the first word vector, the second word vector, and a difference vector. At least one parameter being determined for the model as a function of the specificity predicted for the term and the label of the term.