Abstract: The disclosure relates to transferred learning from a first language (e.g., a source language for which a semantic parser has been defined) to a second language (e.g., a target language for which a semantic parser has not been defined). A system may use knowledge from a trained model in one language to model another language. For example, the system may transfer knowledge of a semantic parser from a first (e.g., source) language to a second (e.g., target) language. Such transfer of knowledge may occur and be useful when the first language has sufficient training data but the second language has insufficient training data. The foregoing transfer of knowledge may extend the semantic parser for multiple languages (e.g., the first language and the second language).

Abstract: The disclosure relates to transferred learning from a first language (e.g., a source language for which a semantic parser has been defined) to a second language (e.g., a target language for which a semantic parser has not been defined). A system may use knowledge from a trained model in one language to model another language. For example, the system may transfer knowledge of a semantic parser from a first (e.g., source) language to a second (e.g., target) language. Such transfer of knowledge may occur and be useful when the first language has sufficient training data but the second language has insufficient training data. The foregoing transfer of knowledge may extend the semantic parser for multiple languages (e.g., the first language and the second language).

Abstract: A computer implemented method of processing a digitally encoded document having a text composed by an author by using a processor to analyse the segmentation, punctuation and linguistics of text and storing the results in a digitally accessible format. Author traits are then predicted using a machine learning system based on the results of the segmentation, punctuation and linguistics analysis of the text.

Abstract: A preferred example of the process flow of the inventive method (1) is depicted in FIG. 1). The first step (2) of the method (1) is to import an email document (3) to be parsed. In the preprocessing step (10) the email (3) is processed to determine the presence of any header text (5) (excluding any header text that may be within the embedded reply chain) or attachments 4, including attached email documents, if any. Once the header text (5), attachments (4) or other forwarded materials have been identified in the preprocessing step (10), these components of the email (3) are categorized by the computer (51) as non-author composed text. Next the process flow of the parsing computer (51) moves to the step of normalization (11). This entails processing the email document (3) to ascertain whether it is in a preferred format and, if the email document (3) is not in the preferred format, converting at least some of the information within the email document to the preferred format.