Abstract: Provided are a data search method and apparatus, an electronic device and a storage medium. The method includes acquiring search data and a search condition and determining a target data set corresponding to the search data; determining each data distance between the search data and a respective query datum included in the target data set; performing data filtering on the each data distance based on the search condition and writing each filtered data distance as a target data distance into a memory; and reading the target data distance stored in the memory, using a query datum corresponding to the target data distance as a target response datum of the search data and displaying the target response datum.

Abstract: A method and apparatus for recognizing a user intention, a device, and a readable storage medium are disclosed. A specific implementation of the method includes: acquiring a plurality of rounds of dialogue data and a satisfaction degree of each round of dialogue data, selecting target dialogue data having a satisfaction degree meeting a set satisfaction condition; labeling input data in the target dialogue data with an intention label; and training the intention recognition model based on the input data in the target dialogue data and the intention label of the input data, so that the trained intention recognition model performs intention recognition on new input data.

Abstract: The present disclosure proposes a method and an apparatus for recommending sample data. The method may include: inputting a plurality of pieces of sample data to be classified into at least one preset classification model, and acquiring a classifying probability of classifying each piece of sample data into each classification model; acquiring a first distance between each piece of sample data and a classifying boundary of each classification model according to the classifying probability of classifying the piece of sample data into the classification model, in which the classifying boundary of the classification model is configured to distinguish positive and negative sample data; computing a target distance for each piece of sample data according to the first distance between each piece of sample data and the classifying boundary of each classification model.

Abstract: Embodiments of the present disclosure provide a method and apparatus for outputting information. A specific embodiment of the method includes: in response to receiving a query, detecting whether there is an entity slot in the query; in response to there being an entity slot in the query, adding the detected entity slot to a candidate slot; detecting, in the query, a relationship-determinative word of an entity; searching in a preset knowledge graph for a peripheral knowledge graph of the candidate slot; and inferring on the basis of the peripheral knowledge graph according to the relationship-determinative word, and outputting an entity word matching the relationship-determinative word.

Abstract: Embodiments of the present disclosure relate to a dialogue interaction method and apparatus, a device and a storage medium, and relate to the field of artificial intelligence technology. The method may include: determining a first semantic encoding of received user information according to a sentence tree; determining a second semantic encoding for responding to the user information from a dialogue tree according to the first semantic encoding, the sentence tree and the dialogue tree being trained and obtained through sentence node information and/or word node information in a logical brain map sample; and determining a target response sentence of the second semantic encoding from the sentence tree, to be used for a dialogue with a user.

Abstract: A method and apparatus for recognizing a user intention, a device, and a readable storage medium are disclosed. A specific implementation of the method includes: acquiring a plurality of rounds of dialogue data and a satisfaction degree of each round of dialogue data, selecting target dialogue data having a satisfaction degree meeting a set satisfaction condition; labeling input data in the target dialogue data with an intention label; and training the intention recognition model based on the input data in the target dialogue data and the intention label of the input data, so that the trained intention recognition model performs intention recognition on new input data.

Abstract: Embodiments of the present disclosure provide a method and apparatus for outputting information. A specific embodiment of the method includes: in response to receiving a query, detecting whether there is an entity slot in the query; in response to there being an entity slot in the query, adding the detected entity slot to a candidate slot; detecting, in the query, a relationship-determinative word of an entity; searching in a preset knowledge graph for a peripheral knowledge graph of the candidate slot; and inferring on the basis of the peripheral knowledge graph according to the relationship-determinative word, and outputting an entity word matching the relationship-determinative word.

Abstract: Embodiments of the present disclosure relate to a dialogue interaction method and apparatus, a device and a storage medium, and relate to the field of artificial intelligence technology. The method may include: determining a first semantic encoding of received user information according to a sentence tree; determining a second semantic encoding for responding to the user information from a dialogue tree according to the first semantic encoding, the sentence tree and the dialogue tree being trained and obtained through sentence node information and/or word node information in a logical brain map sample; and determining a target response sentence of the second semantic encoding from the sentence tree, to be used for a dialogue with a user.

Abstract: The disclosure provides a method, an apparatus, and a storage medium for recommending interactive information. The method includes: obtaining information of a chat statement of a user, the information of the chat statement including content of the chat statement and attribute information of the chat statement; obtaining a target reply statement matched with the content of the chat statement based on a preset matching strategy; inputting the content of the chat statement, the attribute information of the chat statement, and the target reply statement into a preset matching model to obtain recommendation information for a target function; and recommending the interactive information to the user, the interactive information including the target reply statement and the recommendation information for the target function.

Abstract: The present disclosure provides a resource pushing method, a device and a storage medium for a smart device, the method includes: acquiring a first pushing resource according to a preset rule, where the first pushing resource is used by the smart device to interact with a user, pushing the first pushing resource to the user. In the solution, relevant resources are actively acquired according to behavior information of the user, triggering and hotspots, and are pushed to the user, thereby saving the cost in resource querying by the user, and increasing the exposure of platform resources.

Abstract: The present disclosure provides a method and apparatus for correcting a speech recognition error based on artificial intelligence, and a storage medium, wherein the method comprises: obtaining a second speech recognition result of a second speech query input by the user; performing error-correcting intention recognition according to the second speech recognition result; extracting error-correcting information from the second speech recognition result when it is determined that the user has an error-correcting intention; screening error-correcting resources according to the error-correcting information, and using a selected best-matched error-correcting resource to perform error correction for the first speech recognition result, the first speech recognition result being a speech recognition result of a first speech query which is input before the second speech query.

Abstract: The present disclosure provides a resource pushing method, a device and a storage medium for a smart device, the method includes: acquiring a first pushing resource according to a preset rule, where the first pushing resource is used by the smart device to interact with a user, pushing the first pushing resource to the user. In the solution, relevant resources are actively acquired according to behavior information of the user, triggering and hotspots, and are pushed to the user, thereby saving the cost in resource querying by the user, and increasing the exposure of platform resources.

Abstract: The present disclosure proposes a method and an apparatus for recommending sample data. The method may include: inputting a plurality of pieces of sample data to be classified into at least one preset classification model, and acquiring a classifying probability of classifying each piece of sample data into each classification model; acquiring a first distance between each piece of sample data and a classifying boundary of each classification model according to the classifying probability of classifying the piece of sample data into the classification model, in which the classifying boundary of the classification model is configured to distinguish positive and negative sample data; computing a target distance for each piece of sample data according to the first distance between each piece of sample data and the classifying boundary of each classification model.

Abstract: The present disclosure provides a method and apparatus for correcting a speech recognition error based on artificial intelligence, and a storage medium, wherein the method comprises: obtaining a second speech recognition result of a second speech query input by the user; performing error-correcting intention recognition according to the second speech recognition result; extracting error-correcting information from the second speech recognition result when it is determined that the user has an error-correcting intention; screening error-correcting resources according to the error-correcting information, and using a selected best-matched error-correcting resource to perform error correction for the first speech recognition result, the first speech recognition result being a speech recognition result of a first speech query which is input before the second speech query.