Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A system of retrieving documents comprising: coding a plurality of stored documents as a respective document feature vector; generating a query feature vector based on a query document; and performing one or more logical operations between the query feature vector and the document feature vector to obtain respective similarity measures.

Abstract: A method for retrieving based on a search term together with its corresponding meaning from a set of base documents those documents which contain the search term and in which the certain search term has the certain meaning to enable the building of an index on the retrieved documents. The method includes searching for those base documents among the set of base documents which contain the certain search term and evaluating the found base documents as to whether the search term contained in the found base documents, respectively, has a certain meaning. Evaluation includes generating a text document to represent elements surrounding the search term and their corresponding absolute or relative position with respect to the search term; inputting the text document into a trainable classifying apparatus; classifying the inputted text document to judge whether the search term has the inputted meaning.

Abstract: A computer-implemented method of realizing an associative memory capable of storing a set of documents and retrieving one or more stored documents similar to an inputted query document, said method comprising: coding each document or a part of it through a corresponding feature vector consisting of a series of bits which respectively code for the presence or absence of certain features in said document; arranging the feature vectors in a matrix; generating a query feature vector based on the query document and according to the rules used for generating the feature vectors corresponding to the stored documents such that the query vector corresponds in its length to the width of the matrix; storing the matrix column-wise; for those columns of the matrix where the query vector indicates the presence of a feature, bitwise performing one or more of preferably hardware supported logical operations between the columns of the matrix to obtain one or more additional result columns coding for a similarity measure betwe

Abstract: A method for building a classification model for classifying unclassified documents based on the classification of a plurality of documents which respectively have been classified as belonging to one of a plurality of classes, said documents being digitally represented in a computer, said documents respectively comprising a plurality of terms which respectively comprise one or more symbols of a finite set of symbols, and said method comprising the following steps: representing each of said plurality of documents by a vector of n dimensions, said n dimensions forming a vector space, whereas the value of each dimension of said vector corresponds to the frequency of occurrence of a certain term in the document corresponding to said vector, so that said n dimensions span up a vector space; representing the classification of said already classified documents into classes by separating said vector space into a plurality of subspaces by one or more hyperplanes, such that each subspace comprises one or more documents

Abstract: A computer-implemented method of realizing an associative memory capable of storing a set of documents and retrieving one or more stored documents similar to an inputted query document, said method comprising: coding each document or a part of it through a corresponding feature vector consisting of a series of bits which respectively code for the presence or absence of certain features in said document; arranging the feature vectors in a matrix; generating a query feature vector based on the query document and according to the rules used for generating the feature vectors corresponding to the stored document s such that the query vector corresponds in its length to the width of the matrix; storing the matrix column-wise; for those columns of the matrix where the query vector indicates the presence of a feature, bitwise performing one or more of preferably hardware supported logical operations between the columns of the matrix to obtain one or more additional result columns coding for a similarity measure betw