Abstract: A method, a structure, and a computer system for multilingual learning. The exemplary embodiments may include training, for each language in a set of two or more languages, a model for a task and identifying one or more important words appearing in at least two of the models. The exemplary embodiments may further include weighting one or more conflicts and one or more overlaps between the one or more important words, as well as generating a cluster of at least two languages of the set based on an aggregate of the weighting.

Abstract: A computer-implemented method, system and computer program product for generating rules to be utilized by a rule-based system. A parser analyzes a received string of words according to grammatical rules to generate a parse tree. The parse tree may then be reviewed by a user to determine if such a parse tree represents the appropriate syntactic structure of the string of words. If the user believes that the parse tree does not represent the appropriate syntactic structure of the string, then the user, who does not need to possess the expertise of natural language processing, may select the nodes and/or edges of the parse tree in a particular order that corresponds to the correct syntactic structure of the string of words. A grammatical rule is then created for parsing the string of words based on the user selection of nodes and/or edges of the parse tree in a particular order.

Abstract: The approach for automatically adjusting display setting for a user that uses corrective lens or not is disclosed. The approach determines whether a user is wearing corrective visual lens. If the user is not wearing correct visual lens, then the system measures the ocular features of the user via sensors. The system then transmits data associated with the ocular features to a deep learning server wherein the deep learning server analyzes the data. The server generates a customized display setting based on the analyzed data. The system receives the display setting from the deep learning server and automatically adjusts the display setting on a device of the user based on the received display setting.

Abstract: The approach for automatically adjusting display setting for a user that uses corrective lens or not is disclosed. The approach determines whether a user is wearing corrective visual lens. If the user is not wearing correct visual lens, then the system measures the ocular features of the user via sensors. The system then transmits data associated with the ocular features to a deep learning server wherein the deep learning server analyzes the data. The server generates a customized display setting based on the analyzed data. The system receives the display setting from the deep learning server and automatically adjusts the display setting on a device of the user based on the received display setting.