An electronic apparatus is provided. The electronic apparatus includes: a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction to: obtain usage information on an application installed in the electronic apparatus, obtain a natural language understanding model, among a plurality of natural language understanding models, corresponding to the application based on the usage information, perform natural language understanding of a user voice input related to the application based on the natural language understanding model corresponding to the application, and perform an operation of the application based on the preformed natural language understanding.

An electronic apparatus is provided. The electronic apparatus includes: a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction to: obtain usage information on an application installed in the electronic apparatus, obtain a natural language understanding model, among a plurality of natural language understanding models, corresponding to the application based on the usage information, perform natural language understanding of a user voice input related to the application based on the natural language understanding model corresponding to the application, and perform an operation of the application based on the preformed natural language understanding.

A discrete 3-D processor comprises first and second dice. The first die comprises three-dimensional memory (3D-M) arrays, whereas the second die comprises logic circuits and at least an off-die peripheral-circuit component of the 3D-M array(s). The first die does not comprise the off-die peripheral-circuit component. The first and second dice are communicatively coupled by a plurality of inter-die connections. The preferred discrete 3-D processor can be applied to mathematical computing, computer simulation, configurable gate array, pattern processing and neural network.

A discrete 3-D processor comprises first and second dice. The first die comprises three-dimensional memory (3D-M) arrays, whereas the second die comprises logic circuits and at least an off-die peripheral-circuit component of the 3D-M array(s). The first die does not comprise the off-die peripheral-circuit component. The first and second dice are communicatively coupled by a plurality of inter-die connections. The preferred discrete 3-D processor can be applied to mathematical computing, computer simulation, configurable gate array, pattern processing and neural network.

A semantic understanding method and apparatus, and a device and a storage medium are provided. The method includes: acquiring a recognition character string that matches speech information; acquiring, from an entity vocabulary library, at least one entity vocabulary respectively corresponding to each recognition character in the recognition character string; and according to a situation of each entity vocabulary hitting the recognition character string, determining a matching entity vocabulary as a semantic understanding result of the speech information. By means of the method, insofar as a completely matching entity vocabulary is not acquired, a matching entity vocabulary can still be determined according to an entity vocabulary library, and semantic information of speech is thus accurately understood; and the method also has relatively high fault tolerance for situations such as wrong words, added words, and omitted words, such that the semantic understanding accuracy of speech information is improved.

A semantic understanding method and apparatus, and a device and a storage medium are provided. The method includes: acquiring a recognition character string that matches speech information; acquiring, from an entity vocabulary library, at least one entity vocabulary respectively corresponding to each recognition character in the recognition character string; and according to a situation of each entity vocabulary hitting the recognition character string, determining a matching entity vocabulary as a semantic understanding result of the speech information. By means of the method, insofar as a completely matching entity vocabulary is not acquired, a matching entity vocabulary can still be determined according to an entity vocabulary library, and semantic information of speech is thus accurately understood; and the method also has relatively high fault tolerance for situations such as wrong words, added words, and omitted words, such that the semantic understanding accuracy of speech information is improved.

Systems and methods for speech recognition correction include receiving a voice recognition input from an individual user and using a trained error correction model to add a new alternative result to a results list based on the received voice input processed by a voice recognition system. The error correction model is trained using contextual information corresponding to the individual user. The contextual information comprises a plurality of historical user correction logs, a plurality of personal class definitions, and an application context. A re-ranker re-ranks the results list with the new alternative result and a top result from the re-ranked results list is output.

Systems and methods for speech recognition correction include receiving a voice recognition input from an individual user and using a trained error correction model to add a new alternative result to a results list based on the received voice input processed by a voice recognition system. The error correction model is trained using contextual information corresponding to the individual user. The contextual information comprises a plurality of historical user correction logs, a plurality of personal class definitions, and an application context. A re-ranker re-ranks the results list with the new alternative result and a top result from the re-ranked results list is output.

A system described herein may provide for the adaptation and/or expansion of a natural language processing (“NLP”) platform, that supports only a limited quantity of intents, such that the described system may support an unlimited (or nearly unlimited) quantity of intents. For example, a hierarchical structure of agents may be used, where each agent includes multiple intents. A top-level (e.g., master) agent may handle initial user interactions, and may indicate a next-level agent to handle subsequent interactions.

A system described herein may provide for the adaptation and/or expansion of a natural language processing (“NLP”) platform, that supports only a limited quantity of intents, such that the described system may support an unlimited (or nearly unlimited) quantity of intents. For example, a hierarchical structure of agents may be used, where each agent includes multiple intents. A top-level (e.g., master) agent may handle initial user interactions, and may indicate a next-level agent to handle subsequent interactions.