Abstract: System and methods (100) for automatically tagging electronic documents are disclosed. An input module receives (102) an electronic document to be tagged. A preprocessing module then preprocesses (104) the electronic document to be tagged. The preprocessing of the electronic document comprises extracting a text from the electronic document to be tagged, replacing a number or a date in the extracted text with a predetermined symbol, and tokenizing the extracted text with the predetermined symbol into a plurality of tokens. After the preprocessing (104), a deep learning module determines (106) a tag for at least one of the plurality of tokens. The determined tag for the at least one token is then output (108) by an output module.

Abstract: The present disclosure provides a system for predicting a disease state based on speech occurrences. A feature extraction module extracts a plurality of lingual features from a speech record of the speech occurrence. The lingual features are chosen based on a correlation between the lingual features and the disease state in at least a first language and a second language. The lingual features are consistent for transcripts in at least the first language and the second language. A prediction module including a trained classification model generates a prediction of the disease state for speech occurrences in at least the first language and the second language using the lingual features extracted from the speech records.

Abstract: The present disclosure provides a system for predicting a disease state based on speech occurrences. A feature extraction module extracts a plurality of lingual features from a speech record of the speech occurrence. The lingual features are chosen based on a correlation between the lingual features and the disease state in at least a first language and a second language. The lingual features are consistent for transcripts in at least the first language and the second language. A prediction module including a trained classification model generates a prediction of the disease state for speech occurrences in at least the first language and the second language using the lingual features extracted from the speech records.

Abstract: The invention belongs to the field of information system technology and more specifically in the area of electronic content management. The invention concerns method producing filtering systems of electronic documents that contain text in different languages, e.g. English, French, etc., as well as multimedia elements, e.g. digital images and/or digital video and/or digital excerpts of audio/speech. These documents can be semi-structured, i.e., they can exhibit structural features that are not to be found in non-digital documents, e.g. hyperlinks, or not. The method can be applied in the same way and provides the same results either to the filtering of electronic content in the Internet (World Wide Web, electronic mail, etc.) or in organizational computer networks (e.g. intranets), as well as in any other network that allows the transfer of multimedia and/or multilingual electronic content.