Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.

Techniques are disclosed for identifying hypocrisy in text. A computer system creates, from fragments of text, a syntactic tree that represents syntactic relationships between words in the fragments. The system identifies, in the syntactic tree, a first entity and a second entity. The system further determines that the first entity is opposite to the second entity. The system further determines a first sentiment score for a first fragment comprising the first entity and a second sentiment score for a second fragment comprising the second entity. The system, responsive to determining that the first sentiment score and the second sentiment score indicate opposite emotions, identifies the text as comprising hypocrisy and providing the text to an external device.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

Systems and methods are provided for performing operations including: receiving, by a messaging application, input that selects an option to generate a message using an avatar with a caption; presenting, by the messaging application, the avatar and a caption entry region proximate to the avatar; populating, by the messaging application, the caption entry region with a text string comprising one or more words; determining, by the messaging application, context based on the one or more words in the text string; and modifying, by the messaging application, an expression of the avatar based on the determined context.

Systems and methods are provided for performing operations including: receiving, by a messaging application, input that selects an option to generate a message using an avatar with a caption; presenting, by the messaging application, the avatar and a caption entry region proximate to the avatar; populating, by the messaging application, the caption entry region with a text string comprising one or more words; determining, by the messaging application, context based on the one or more words in the text string; and modifying, by the messaging application, an expression of the avatar based on the determined context.

A natural language model can be primed utilizing optimized examples generated from a labeled knowledge graph corresponding to an independently developed application program. Parsing of the labeled knowledge graph can include the identification of triples, comprising a source node, a destination node, and a link between them, each of which can be labeled. One or more natural language input examples can be generated from an individual triple by concatenating the natural language words or phrases utilized to label the source node in the link. Determinations that subsequently received natural language user input is similar to the generated examples can result in an identification of the triple, which can, in turn, trigger the performance of a function associated with the destination node of the triple. Labels can include preferred labels and alternative labels, and various permutations thereof can be concatenated to generate alternative natural language input examples.

Techniques for performing outputting additional content associated with but nonresponsive to an input command are described. A system receives input data from a device. The system determines an intent representing the input data and receives first output data responsive to the input data. The system determines, based on context data, that additional content associated with the first output data but nonresponsive to the input data should be output. The system receives second output data associated with but nonresponsive to the input data thereafter. The system then presents first content corresponding to the first output data and second content corresponding to the second output data.

Exemplary embodiments relate to a system for recovering a conversation between a user and the system when the system is unable to properly respond to a user's input. The system may process the user input and determine an error condition exists. The system may query one or more storage systems to identify candidate text data based on their semantic similarity to the user input. The storage systems may store data related to past frequently entered inputs and/or user-generated inputs. Alternative text data is selected from the candidate text data, and presented to the user for confirmation.

A conversation engine performs conversations with users using chatbots customized for performing a set of tasks that can be performed using an online system. The conversation engine loads a chatbot configuration that specifies the behavior of a chatbot including the tasks that can be performed by the chatbot, the types of entities relevant to each task, and so on. The conversation may be voice based and use natural language. The conversation engine may load different chatbot configurations to implement different chatbots. The conversation engine receives a conversation engine configuration that specifies the behavior of the conversation engine across chatbots. The system may be a multi-tenant system that allows customization of the chatbots for each tenant.