Patent ID: 11875265
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. 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 encompassing a text from a domain, each of the identifiers characterizing a degree of association to at least three classes for the term candidates that differ from one another, the different classes indicating different degrees of association of the term candidate to the domain, the training data including an assignment of the features to the identifiers, the method comprising the following steps: predefining a feature of the features to an input layer of the artificial neural network; assigning, by the artificial neural network, an identifier of the identifiers to the feature from the input layer in a prediction in an output layer of the artificial neural network; comparing the identifier from the output layer with the identifier assigned to the feature in the training data; and learning, as a function of the result of the comparison, at least one parameter of the artificial neural network, which characterizes a connection of the artificial neural network between the input layer and the output layer; wherein the term candidates are compounds including at least two components; wherein the compounds are from a corpus subject-specific to the domain and are split as term candidates into components, the compounds are assigned to at least one of the classes, the features for the compounds and the components are determined; and wherein a productivity and a frequency of the components are determined as the features for the compounds and the components based on the corpus.