CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority to Chinese Patent Application Serial No. 201910130355.5, filed on Feb. 21, 2019, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a field of artificial intelligence technology, and more particularly, to a query processing method, a query processing device, and a computer readable medium.

BACKGROUND

Recently, with the development of artificial intelligence technology, computers have achieved many results having application value in natural language understanding.

Due to the diversity and complexity of semantic and sentence structure, there are certain difficulties in extracting text directly. Methods in the related art mainly focus on extracting keywords in text, and the methods are mainly divided into two categories, i.e., statistical analysis-base method and semantic analysis-base method.

SUMMARY

Embodiments of the present disclosure provide a query processing method, a query processing device, and a computer readable medium.

In a first aspect, the embodiments of the present disclosure provide a query processing method. The method includes: determining a query feedback based on a received query request comprising a character string; determining a target candidate entity set based on the query feedback, the character string and a preset knowledge base; evaluating each target candidate entity in the target candidate entity set to obtain evaluation results, in which a target candidate entity corresponds to an evaluation result; and determining a category of each target candidate entity based on the evaluation result of the target candidate entity.

In some embodiments, determining the target candidate entity set based on the query feedback, the character string and the preset knowledge base, includes: determining a first candidate entity subset based on the query feedback and the character string; determining a second candidate entity subset from the preset knowledge base based on the character string; and determining the target candidate entity set based on the first candidate entity subset and the second candidate entity subset.

In some embodiments, evaluating each target candidate entity in the target candidate entity set, includes: inputting the target candidate entity set and the query feedback respectively to a dual-channel model, to obtain the evaluation results corresponding to respective target candidate entities.

In some embodiments, determining the category of each target candidate entity based on the evaluation result of the target candidate entity, includes: comparing the evaluation result of a target candidate entity with a preset result; and determining the target candidate entity as an entity of a first category when the evaluation result of the target candidate entity meets at least one conditions of: the evaluation result of the target candidate entity being superior to the preset result; and the evaluation result of the target candidate entity being identical to the preset result.

In some embodiments, after comparing the evaluation result of the target candidate entity with the preset result, the method includes: determining the target candidate entity as an entity of a second category when the evaluation result of the target candidate entity is inferior to the preset result.

In some embodiments, after determining the target candidate entity as the entity of the first category the method further includes: determining a search intent entity corresponding to the received query request based on the target candidate entity set and the evaluation results corresponding to respective target candidate entities.

In some embodiments, determining the search intent entity corresponding to the received query request based on the target candidate entity set and the evaluation results corresponding to respective target candidate entities, includes: selecting an optimal evaluation result from the evaluation results corresponding to respective target candidate entities; and determining the target candidate entity corresponding to the optimal evaluation result as the search intent entity.

In some embodiments, the query feedback includes one or more of news, information, related search, encyclopedia, and post bar.

In a second aspect, the embodiments of the present disclosure provide a query processing device. The device includes: a searching module, configured to determine a query feedback based on a received query request comprising a character string; a determining module, configured to determine a target candidate entity set based on the query feedback, the character string and a preset knowledge base; an evaluating module, configured to evaluate each target candidate entity in the target candidate entity set to obtain evaluation results, in which a target candidate entity corresponds to an evaluation result; and a classifying module, configured to determine a category of each target candidate entity based on the evaluation result of the target candidate entity.

In some embodiments, the determining module is configured to: determine a first candidate entity subset based on the query feedback and the character string; determine a second candidate entity subset from the preset knowledge base based on the character string; and determine the target candidate entity set based on the first candidate entity subset and the second candidate entity subset.

In some embodiments, the evaluating module is configured to input the target candidate entity set and the query feedback respectively to a dual-channel model, to obtain the evaluation results corresponding to respective target candidate entities.

In some embodiments, the classifying module is configured to compare the evaluation result of a target candidate entity with a preset result; and determine the target candidate entity as an entity of a first category when the evaluation result of the target candidate entity meets at least one conditions of: the evaluation result of the target candidate entity being superior to the preset result; and the evaluation result of the target candidate entity being identical to the preset result.

In some embodiments, the classifying module is configured to determine the target candidate entity as an entity of a second category when the evaluation result of the target candidate entity is inferior to the preset result.

In some embodiments, the classifying module is configured to determine a search intent entity corresponding to the received query request based on the target candidate entity set and the evaluation results corresponding to respective target candidate entities.

In some embodiments, the classifying module is configured to: select an optimal evaluation result from the evaluation results corresponding to respective target candidate entities; and determine the target candidate entity corresponding to the optimal evaluation result as the search intent entity.

In a third aspect, the embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon. When the computer program is executed by a processor, the above method is implemented.

The technical solution according to the embodiments of the present disclosure can determine the category of the query, so as to accurately determine the intent of the user to input the query, which may reduce the search cost, such as computing resources, network resources, and query time, thereby improving user experience.

Specifically, the solution for acquiring the target candidate entity set adopted by the embodiments of the present disclosure can accurately select the target candidate entity corresponding to the query by combining with the features such as the query feedback and the knowledge base. Moreover, based on the solution for obtaining the evaluation result of each target candidate entity adopted by the embodiments of the present disclosure, it is possible to efficiently and accurately evaluate each target candidate entity, thereby realizing the technical effect of accurately determining the category of the query. Since the categories of the queries can be accurately distinguished, the efficiency of searching can be improved and the search cost can be reduced for the user. Furthermore, since the category of the query can be accurately determined, the accuracy of the result pushed to the user can be improved and the recall rate can be improved.

