Abstract: A semantic classification model training method includes that a sample query template and a label category of at least one category to be predicted in the sample query template are acquired, where the sample query template is constructed according to a sample query statement and a number of the at least one category to be predicted; the sample query template is input to the pre-constructed semantic classification model to obtain a sample semantic category of the at least one category to be predicted; and the semantic classification model is trained according to the sample semantic category and the label category of the at least one category to be predicted.

Abstract: The present disclosure provides an error correction method. The error correction method includes: determining a plurality of target candidate entities from a preset dictionary tree based on a query request; for each target candidate entity, calculating a first probability that the target candidate entity is a legitimate entity; evaluating each target candidate entity to obtain an evaluation result, a target candidate entity corresponding to an evaluation result; and determining a real intent entity corresponding to the query request based on the first probability and the evaluation result.

Abstract: The present disclosure provides a query processing method. The method includes: determining a query feedback based on a received query request including 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; and determining a category of each target candidate entity based on the evaluation result of the target candidate entity.

Abstract: A method for a semantic retrieval, a device and a storage medium are provided. The method may include: receiving query information, and performing sequence labeling on the query information based on a pre-constructed knowledge graph to obtain a sequence labeling result, where the sequence labeling result includes a predetermined information part of the knowledge graph and a semantic retrieval part; constructing a set of a candidate entity matching the sequence labeling result based on the knowledge graph; and performing sematic matching between an entity in the set of the candidate entity and the semantic retrieval part in the sequence labeling result to obtain a set of an entity having a semantic relevance higher than a preset threshold.

Abstract: The present disclosure provides a method and an apparatus for recommending an entity, an electronic device and a computer readable medium. The method includes: determining a request entity, determining at least two characteristics of the request entity and determining a first vector corresponding to the request entity according to the at least two characteristics of the request entity; determining a plurality of candidate entities, determining at least one characteristic for each of the plurality of candidate entities, and determining a second vector corresponding to each of the plurality of candidate entities according to the characteristic of the candidate entity; determining a similarity between the second vector and the first vector; selecting at least one target entity from the plurality of candidate entities according to the similarity between the second vector and the first vector; and recommending the target entity.

Abstract: The present disclosure provides an error correction method. The error correction method includes: determining a plurality of target candidate entities from a preset dictionary tree based on a query request; for each target candidate entity, calculating a first probability that the target candidate entity is a legitimate entity; evaluating each target candidate entity to obtain an evaluation result, a target candidate entity corresponding to an evaluation result; and determining a real intent entity corresponding to the query request based on the first probability and the evaluation result.

Abstract: The present disclosure provides a query processing method. The method includes: determining a query feedback based on a received query request including 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; and determining a category of each target candidate entity based on the evaluation result of the target candidate entity.