Abstract: A method, a computing device, and a non-transitory machine-readable medium for detecting personal information. Terms that are of interest are extracted from a corpus of raw text that has been extracted from a collection of documents. For each of the terms, a surrounding sentence is extracted to form a target sentence to thereby form a plurality of target sentences. The surrounding sentence includes at least one reference to a data subject. A matrix of feature information is generated for each of the target sentences to form a plurality of matrices. A neural network model is trained, using the matrices as input, to compute an output that indicates a likelihood of a given sentence containing personal information.

Abstract: A method, a computing device, and a non-transitory machine-readable medium for detecting personal information. Terms that are of interest are extracted from a corpus of raw text that has been extracted from a collection of documents. For each of the terms, a surrounding sentence is extracted to form a target sentence to thereby form a plurality of target sentences. The surrounding sentence includes at least one reference to a data subject. A matrix of feature information is generated for each of the target sentences to form a plurality of matrices. A neural network model is trained, using the matrices as input, to compute an output that indicates a likelihood of a given sentence containing personal information.

Abstract: A method, a computing device, and a non-transitory machine-readable medium for classifying documents. A document collection is sorted into a plurality of categories. A classifier corresponding to a category of the plurality of categories is trained to output a probability that a document associated with the category is of a selected type (e.g., confidential).

Abstract: A method, a computing device, and a non-transitory machine-readable medium for detecting personal information. Terms that are of interest are extracted from a corpus of raw text that has been extracted from a collection of documents. For each of the terms, a surrounding sentence is extracted to form a target sentence to thereby form a plurality of target sentences. The surrounding sentence includes at least one reference to a data subject. A matrix of feature information is generated for each of the target sentences to form a plurality of matrices. A neural network model is trained, using the matrices as input, to compute an output that indicates a likelihood of a given sentence containing personal information.

Abstract: A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a plurality of electronic documents, apply a trained machine learning classifier to automatically classify at least some of said plurality of electronic documents, wherein said machine learning classifier comprises two or more attention layers, and wherein at least one of the attention layers comprises an adjustable parameter which controls a distribution of attention weights assigned by said attention layer.

Abstract: A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a plurality of electronic documents, apply a trained machine learning classifier to automatically classify at least some of said plurality of electronic documents, wherein said machine learning classifier comprises two or more attention layers, and wherein at least one of the attention layers comprises an adjustable parameter which controls a distribution of attention weights assigned by said attention layer.

Abstract: A method comprising operating at least one hardware processor for: receiving, as input, a plurality of electronic documents, training a machine learning classifier based, at least on part, on a training set comprising: (i) labels associated with the electronic documents, (ii) raw text from each of said plurality of electronic documents, and (iii) a rasterized version of each of said plurality of electronic documents, and applying said machine learning classifier to classify one or more new electronic documents.

Abstract: A method comprising operating at least one hardware processor for: receiving, as input, a plurality of electronic documents, training a machine learning classifier based, at least on part, on a training set comprising: (i) labels associated with the electronic documents, (ii) raw text from each of said plurality of electronic documents, and (iii) a rasterized version of each of said plurality of electronic documents, and applying said machine learning classifier to classify one or more new electronic documents.