Search method and apparatus based on artificial intelligence

    公开(公告)号:US11151177B2

    公开(公告)日:2021-10-19

    申请号:US16054842

    申请日:2018-08-03

    Abstract: Embodiments of the present disclosure disclose a search method and apparatus based on artificial intelligence. A specific implementation of the method comprises: acquiring at least one candidate document related to a query sentence; determining a query word vector sequence corresponding to a segmented word sequence of the query sentence, and determining a candidate document word vector sequence corresponding to a segmented word sequence of each candidate document in the at least one candidate document; performing a similarity calculation for each candidate document in the at least one candidate document; selecting, in a descending order of similarities between the candidate document and the query sentence, a preset number of candidate documents from the at least one candidate document as a search result.

    Distributed database transaction processing method, device based on GPS atomic clock server

    公开(公告)号:US10567549B2

    公开(公告)日:2020-02-18

    申请号:US16282438

    申请日:2019-02-22

    Abstract: A distributed database transaction processing method and device based on a GPS atomic clock, the method includes: receiving a plurality of transaction requests by the first node server; determining a concurrency conflict between transaction requests received by the first node server; obtaining start time of each transaction in a case that the concurrency conflict exists, wherein the start time is local time of the second node server when the transaction request is sent; and local time of the second node server is synchronized with time of the GPS atomic clock server in an area which the second node server locates; processing all transactions by the first node server according to the sequence of the start time of each transaction. Throughput of distributed database system can be increased and an arrangement complexity of servers is reduced.

    METHOD AND APPARATUS FOR TRAINING DEEP LEARNING MODEL

    公开(公告)号:US20210216875A1

    公开(公告)日:2021-07-15

    申请号:US17218033

    申请日:2021-03-30

    Abstract: A method for training a deep learning model may include: acquiring model description information and configuration information of a deep learning model; segmenting the model description information into at least two sections based on segmentation point variable in the configuration information, and loading the model description information to a corresponding resource to run; inputting a batch of training samples into a resource corresponding to a first section of model description information, then starting training and using obtained context information as an input of a resource corresponding to a subsequent section of model description information; and so on until an operation result of a resource corresponding to a final section of model description information is obtained; if a training completion condition is met, outputting a trained deep learning model; and otherwise, keeping on acquiring a subsequent batch of training samples and performing the above training steps until the condition is met.

    Artificial intelligence based method and apparatus for generating information

    公开(公告)号:US10528667B2

    公开(公告)日:2020-01-07

    申请号:US15900176

    申请日:2018-02-20

    Abstract: An artificial intelligence based method and apparatus for generating information are disclosed. The method in an embodiment includes: segmenting a to-be-processed text into characters to obtain a character sequence; determining a character vector for each character in the character sequence to generate a character vector sequence; generating a plurality of character vector subsequences by segmenting the character vector sequence based on a preset vocabulary; for each generated character vector subsequence, determining a sum of character vectors composing the character vector subsequence as a target vector, and inputting the target vector into a pre-trained first neural network to obtain a word vector corresponding to the each character vector subsequence, the first neural network used to characterize a correspondence between the target vector and the word vector; and analyzing the to-be-processed text based on the obtained word vector to generate an analysis result. This embodiment improves the adaptability of text processing.

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