Concepts for recreating electronic content are presented. One example comprises identifying one or more content references in one or more content sources, wherein each of the one or more content references is associated with a content item. The method then comprises, for each of the one or more identified content references, retrieving the associated content item, then normalizing the one or more retrieved content items to obtain one or more normalized content items. The method then comprises recreating electronic content by combining the one or more normalized content items.

Concepts for recreating electronic content are presented. One example comprises identifying one or more content references in one or more content sources, wherein each of the one or more content references is associated with a content item. The method then comprises, for each of the one or more identified content references, retrieving the associated content item, then normalizing the one or more retrieved content items to obtain one or more normalized content items. The method then comprises recreating electronic content by combining the one or more normalized content items.

Described in detail herein is a method of copying data of one or more virtual machines being hosted by one or more non-virtual machines. The method includes receiving an indication that specifies how to perform a copy of data of one or more virtual machines hosted by one or more virtual machine hosts. The method may include determining whether the one or more virtual machines are managed by a virtual machine manager that manages or facilitates management of the virtual machines. If so, the virtual machine manager is dynamically queried to automatically determine the virtual machines that it manages or that it facilitates management of. If not, a virtual machine host is dynamically queried to automatically determine the virtual machines that it hosts. The data of each virtual machine is then copied according to the specifications of the received indication.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

An information search system, including: a database (12); a query sentence acceptance unit (26) that accepts a query sentence; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a shared keyword dictionary (30) in which relevant keywords are registered in association with each other; a local keyword dictionary (102) in which district keywords used in particular districts are registered; a candidate search keyword reader (32) that reads out a keyword that is relevant to the inputted search keyword; and a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword, wherein, in a case in which the inputted search keyword is not registered in the local keyword dictionary, the candidate search keyword reader refers to the shared keyword dictionary, so as to read out a keyword that is relevant to the inputted search keyword.

The present disclosure relates to concurrent learning of a relationship estimation model and a phrase generation model. The relationship estimation model estimates a relationship between phrases. The phrase generation model generates a phrase that relates to an input phrase. The phrase generation model includes an encoder and a decoder. The encoder converts a phrase into a vector using a three-piece set as learning data. The decoder generates, based on the converted vector and a connection expression or a relationship label, a phrase having a relationship expressed by the connection expression or the relationship label for the phrase. The relationship estimation model generates a relationship score from the converted vector, which indicates each phrase included in a combination of the phrases, and a vector indicating the connection expression and the relationship label.

A method, system and computer program for automatic, highly accurate machine scans of unstructured text data sources, like information kept or displayed in Web browsers, WORD, POWERPOINT, EXCEL, PDF, and other documents, with the ability to detect, isolate and extract specific text information from unknown and varying locations within the unstructured text data. The system uses multiple human-vision-like but electronic scans of the unstructured data using artificial intelligence techniques to locate, and extract required information despite varying conditions, like unknown number of pages, unknown sequence of pages, unknown data layouts and data arrangements, unknown number, lengths and indentations of sections/paragraphs, and in case of tabular data, unknown number of rows and column sequences in the unstructured text data source.

A method, system and computer program for automatic, highly accurate machine scans of unstructured text data sources, like information kept or displayed in Web browsers, WORD, POWERPOINT, EXCEL, PDF, and other documents, with the ability to detect, isolate and extract specific text information from unknown and varying locations within the unstructured text data. The system uses multiple human-vision-like but electronic scans of the unstructured data using artificial intelligence techniques to locate, and extract required information despite varying conditions, like unknown number of pages, unknown sequence of pages, unknown data layouts and data arrangements, unknown number, lengths and indentations of sections/paragraphs, and in case of tabular data, unknown number of rows and column sequences in the unstructured text data source.

Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.