公开(公告)号：US10606949B2

公开(公告)日：2020-03-31

申请号：US15921386

申请日：2018-03-14

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Zhifan Zhu ,  Shikun Feng ,  Kunsheng Zhou ,  Jingzhou He

IPC: G06F17/27 ,  G06N3/08 ,  G06F17/22 ,  G06N3/04 ,  G06N3/02

Abstract: This disclosure discloses an artificial intelligence based method and apparatus for checking a text. An embodiment of the method comprises: lexing a first to-be-checked text and a second to-be-checked text respectively, determining word vectors of the lexed words to generate a first word vector sequence and a second word vector sequence; inputting the first word vector sequence and the second word vector sequence respectively into a pre-trained convolutional neural network containing at least one multi-scale convolutional layer, identifying vector sequences in a plurality of vector sequences outputted by a last multi-scale convolutional layer as eigenvector sequences, to obtain eigenvector sequence groups respectively corresponding to the texts; combining eigenvector sequences in each eigenvector sequence group to generate a combined eigenvector sequence; and analyzing the generated combined eigenvector sequences to determine whether the first text and the second text pass a similarity check. The embodiment improves the flexibility in checking a text.

公开(公告)号：US20190057164A1

公开(公告)日：2019-02-21

申请号：US16054559

申请日：2018-08-03

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Kunsheng Zhou ,  Shikun Feng ,  Zhifan Zhu ,  Jingzhou He

IPC: G06F17/30 ,  G06K9/62 ,  G06F15/18

Abstract: The disclosure discloses a search method and apparatus based on artificial intelligence. An embodiment of the method includes: receiving search information entered by a user; determining a candidate to-be-pushed message set based on the search information; predicting a probability of being clicked for a candidate to-be-pushed message in the candidate to-be-pushed message set using a pre-trained scoring model based on the search information and the candidate to-be-pushed message set, the scoring model being obtained by training based on a pre-stored first search information set, a to-be-pushed message set corresponding to a piece of first search information in the first search information set, and a preset priority of a to-be-pushed message in the to-be-pushed message set; and selecting a preset number of the candidate to-be-pushed messages to form a message sequence in descending order of the probability of being clicked, and pushing the message sequence to a terminal of the user.