The present disclosure provides a corpus cleaning method and a corpus entry system. The method includes: obtaining an input utterance; generating a predicted value of an information amount of each word in the input utterance according to the context of the input utterance using a pre-trained general model; and determining redundant words according to the predicted value of the information amount of each word, and determining whether to remove the redundant words from the input utterance. In such a manner, the objectivity and accuracy of corpus cleaning can be improved.

An intelligent reminding method is provided, which is applicable to a first electronic device, and includes: receiving a message sent by a second electronic device, where the message is a first message received by a first application, and the first message includes a task that needs to be processed by a first user; determining whether there is first interaction information in the first electronic device, where an occurrence time of the first interaction information is later than a time point when the first message is received, and an interaction object of the first interaction information is a second user operating the second electronic device; and presenting reminding information in a case that there is not the first interaction information in the first electronic device, where the reminding information is used for reminding the first user that the task is not completed.

A computer divides a character string included in text data into a plurality of tokens. The computer searches, by performing matching processing between a token string indicating a specific number of consecutive tokens among the plurality of tokens and dictionary information including a plurality of named entities, the plurality of named entities for a similar named entity whose similarity to the token string is equal to or more than a threshold. The computer converts matching information indicating a result of the matching processing between the token string and the similar named entity into first vector data. The computer generates input data by using a plurality of pieces of vector data converted from the plurality of tokens and the first vector data. The computer generates a named entity recognition model that detects a named entity by performing machine learning using the input data.

A computer divides a character string included in text data into a plurality of tokens. The computer searches, by performing matching processing between a token string indicating a specific number of consecutive tokens among the plurality of tokens and dictionary information including a plurality of named entities, the plurality of named entities for a similar named entity whose similarity to the token string is equal to or more than a threshold. The computer converts matching information indicating a result of the matching processing between the token string and the similar named entity into first vector data. The computer generates input data by using a plurality of pieces of vector data converted from the plurality of tokens and the first vector data. The computer generates a named entity recognition model that detects a named entity by performing machine learning using the input data.

Systems, methods, and apparatus for communication assistance for aneurotypical individuals are described. Embodiments of the systems, methods, and apparatus may receive input data during a communication between a first user and a second user, generate feedback based on the input data using a shared network comprising psychological information about the second user, wherein the shared network is based at least in part on interactions between the second user and a third user, and provide the feedback to the first user during the communication.

Systems, methods, and apparatus for communication assistance for aneurotypical individuals are described. Embodiments of the systems, methods, and apparatus may receive input data during a communication between a first user and a second user, generate feedback based on the input data using a shared network comprising psychological information about the second user, wherein the shared network is based at least in part on interactions between the second user and a third user, and provide the feedback to the first user during the communication.

A computer-implemented method of mapping attribute names of a source data model to a destination data model includes obtaining multiple source attribute names from the source data model, and obtaining multiple destination attribute names from the destination data model. The destination data model includes multiple attributes that correspond to attributes in the source data model having different attribute names. The method includes processing the obtained source attribute names and the obtained destination attribute names to standardize the attribute names according to specified character formatting, supplying the standardized attribute names to a machine learning network model to predict a mapping of each source attribute name to a corresponding one of the destination attribute names, and outputting, according to mapping results of the machine learning network model, an attribute mapping table indicating the predicted destination attribute name corresponding to each source attribute name.

A computer-implemented method of mapping attribute names of a source data model to a destination data model includes obtaining multiple source attribute names from the source data model, and obtaining multiple destination attribute names from the destination data model. The destination data model includes multiple attributes that correspond to attributes in the source data model having different attribute names. The method includes processing the obtained source attribute names and the obtained destination attribute names to standardize the attribute names according to specified character formatting, supplying the standardized attribute names to a machine learning network model to predict a mapping of each source attribute name to a corresponding one of the destination attribute names, and outputting, according to mapping results of the machine learning network model, an attribute mapping table indicating the predicted destination attribute name corresponding to each source attribute name.

A sequence of context based search verb and search terms are selected via either touch or voice selection in a mobile wireless device and then a human articulated voice query is expanded using a culture and a world intelligence dictionary for conducting more efficient searches. Focus groups are used for populating prior query search databases for storage in the mobile wireless device that are organized by context based search terms in a mobile wireless device for efficient search.

A sequence of context based search verb and search terms are selected via either touch or voice selection in a mobile wireless device and then a human articulated voice query is expanded using a culture and a world intelligence dictionary for conducting more efficient searches. Focus groups are used for populating prior query search databases for storage in the mobile wireless device that are organized by context based search terms in a mobile wireless device for efficient search.