Abstract: Aspects of the present disclosure relate generally to reminder systems and, more particularly, to a contextual item discovery and pattern inculcated reminder mechanism and methods of use. A computer-implemented method includes: determining, by a computing device, an event in which a user will participate; associating, by the computing device, one or more items with the event; determining a location of the one or more items associated with the event including that the user does not possess any combination of the one or more items; and providing an alert to the user that the any of the one or more items associated with the event is not in the possession of the user.

Abstract: A system and method for providing a text to speech output by receiving user audio data, determining a user region-specific-pronunciation classification according to the audio data, determining text for a response to the user according to the audio data, identifying a portion from the text, where a region specific-pronunciation dictionary includes the portion, and using a phoneme string, from the dictionary selected according to the user region-specific pronunciation classification, for the word in a text to speech output to the user.

Abstract: A system and method for providing a text to speech output by receiving user audio data, determining a user region-specific-pronunciation classification according to the audio data, determining text for a response to the user according to the audio data, identifying a portion from the text, where a region specific-pronunciation dictionary includes the portion, and using a phoneme string, from the dictionary selected according to the user region-specific pronunciation classification, for the word in a text to speech output to the user.

Abstract: A method, system, and computer-usable medium are disclosed for processing a search query that includes context based exclusion criteria. A search query is analyzed as to a subject, topic, noun phrase and query intent. Negative exclusion context and emphasis of the search query are determined with a filter word list to refine the query intent. A knowledge graph is created based on the search query. A set of branches of the knowledge graph is determined from which information is retrieved. Negative intent branches are identified based on the exclusion context and pruning is performed on the negative intent branches from the knowledge graph.

Abstract: A method, system, and computer-usable medium are disclosed for processing a search query that includes context based exclusion criteria. A search query is analyzed as to a subject, topic, noun phrase and query intent. Negative exclusion context and emphasis of the search query are determined with a filter word list to refine the query intent. A knowledge graph is created based on the search query. A set of branches of the knowledge graph is determined from which information is retrieved. Negative intent branches are identified based on the exclusion context and pruning is performed on the negative intent branches from the knowledge graph.

Abstract: Concepts for managing a supervised machine learning model of a set of documents are presented. A system obtains annotated versions of the documents, the documents being annotated by annotators. A conflict between a plurality of annotations of the annotated versions of the documents is identified. The machine learning model includes a set of entities and relations defining relationships between entities. The identified conflict is resolved by at least one of identifying the correct annotation between the conflicting options, splitting the annotated text into two separate entities or relations, generating a new entity at the same or a less specific hierarchical level as the entities or relation in conflict, and/or changing an annotation of the annotated version of the document.

Abstract: Concepts for managing a supervised machine learning model of a set of documents are presented. A system obtains annotated versions of the documents, the documents being annotated by annotators. A conflict between a plurality of annotations of the annotated versions of the documents is identified. The machine learning model includes a set of entities and relations defining relationships between entities. The identified conflict is resolved by at least one of identifying the correct annotation between the conflicting options, splitting the annotated text into two separate entities or relations, generating a new entity at the same or a less specific hierarchical level as the entities or relation in conflict, and/or changing an annotation of the annotated version of the document.