公开(公告)号：US11537792B2

公开(公告)日：2022-12-27

申请号：US16935040

申请日：2020-07-21

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

Inventor： Can Gao ,  Hao Liu ,  Bolei He ,  Xinyan Xiao ,  Hao Tian

IPC: G06F40/00 ,  G06F40/242 ,  G06F40/279

Abstract: The present disclosure provides a pre-training method for a sentiment analysis model and an electronic device, which relates to a field of artificial intelligence technologies. The method includes: based on a given seed sentiment dictionary, performing sentimental knowledge detection on a training corpus in a training corpus set, and determining a detection sentiment word and a detection word pair of the training corpus; according to preset mask processing rules, performing mask process on the training corpus to generate a masked corpus; performing encoding and decoding on the masked corpus by using a preset encoder and decoder to determine the detection sentiment word and the detection word pair of the training corpus; and updating the preset encoder and decoder according to a difference between prediction sentiment word and the detection sentiment word, and a difference between prediction word pair and the detection word pair.

公开(公告)号：US11797607B2

公开(公告)日：2023-10-24

申请号：US17211612

申请日：2021-03-24

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

Inventor： Huan Liu ,  Mingquan Cheng ,  Kunbin Chen ,  Zhun Liu ,  Bolei He ,  Wei He

IPC: G06F16/901 ,  G06F18/21 ,  G06F18/25 ,  G06F18/2132 ,  G06F40/30 ,  G06T7/00 ,  G06V30/413 ,  G06V30/40 ,  G06V30/18 ,  G06V30/19 ,  G06V10/82 ,  G06V30/10

CPC classification number: G06F16/901 ,  G06F18/217 ,  G06F18/253 ,  G06F40/30 ,  G06T7/0002 ,  G06V10/82 ,  G06V30/18057 ,  G06V30/19173 ,  G06V30/40 ,  G06V30/413 ,  G06F18/2132 ,  G06T2207/30168 ,  G06T2207/30176 ,  G06V30/10

Abstract: Embodiments of the present disclosure disclose a method and apparatus for constructing a quality evaluation model, an electronic device and a computer-readable storage medium. A specific implementation mode of the method comprises: acquiring samples of knowledge contents; extracting statistical features, semantic features, and image features respectively from the samples of knowledge contents; and constructing a quality evaluation model for knowledge according to the statistical features, the semantic features, and the image features. On the basis of the prior art, this implementation mode additionally uses semantic features and image features of knowledge contents to construct a more accurate quality evaluation model based on multi-dimensional features that characterize the actual quality of a knowledge, which may well discover some brief but very useful summary knowledge in an enterprise and may recommend high-quality knowledge more accurately for employees in the enterprise.

公开(公告)号：US11508153B2

公开(公告)日：2022-11-22

申请号：US17115263

申请日：2020-12-08

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

Inventor： Chengxiang Liu ,  Hao Liu ,  Bolei He

IPC: G06V20/40 ,  G06K9/62 ,  G06V30/262 ,  G06V40/16

Abstract: A method for generating a tag of a video, an electronic device, and a storage medium are related to a field of natural language processing and deep learning technologies. The detailed implementing solution includes: obtaining multiple candidate tags and video information of the video; determining first correlation information between the video information and each of the multiple candidate tags; sorting the multiple candidate tags based on the first correlation information to obtain a sort result; and generating the tag of the video based on the sort result.

公开(公告)号：US11216504B2

公开(公告)日：2022-01-04

申请号：US16705749

申请日：2019-12-06

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

Inventor： Guocheng Niu ,  Bolei He ,  Chengxiang Liu ,  Xinyan Xiao ,  Yajuan Lyu

IPC: G06F16/36 ,  G06F40/30 ,  G06F40/295 ,  G06N3/08

Abstract: A document recommendation method based on a semantic tag and a document recommendation device. The method includes: for each document, acquiring a first candidate tag set corresponding to the document, and processing each first candidate tag in the first candidate tag set corresponding to the document to obtain a second candidate tag set corresponding to the document; performing normalization processing on each second candidate tag in the second candidate tag set corresponding to the document to obtain a third candidate tag set corresponding to the document; performing expanding process on each third candidate tag in the third candidate tag set corresponding to the document, and acquiring a fourth candidate tag set corresponding to the document, to form a document library having semantic tags; and recommending a target document obtained from the document library having semantic tags to the user, according to historical semantic tag.

公开(公告)号：US11507751B2

公开(公告)日：2022-11-22

申请号：US16938355

申请日：2020-07-24

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

Inventor： Hao Liu ,  Bolei He ,  Xinyan Xiao

IPC: G06F40/30 ,  G06F40/289 ,  G06F40/242 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure discloses a comment information processing method and apparatus, and a medium. The specific implementation solution is: in response to a user operation, determining an opinion category corresponding to each opinion phrase in a comment opinion dictionary; obtaining a target corpus matching each opinion phrase from a plurality of comment corpora; for each opinion phrase, using a corresponding opinion category to label the target corpus matching each opinion phrase to obtain a first training sample; and training a classification model with the first training sample to identify the opinion category of a comment by using a trained classification model.

公开(公告)号：US11341366B2

公开(公告)日：2022-05-24

申请号：US16988774

申请日：2020-08-10

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

Inventor： Guocheng Niu ,  Bolei He ,  Xinyan Xiao

IPC: G06K9/62 ,  G06T7/73 ,  G06V10/40 ,  G06V30/262 ,  G06V30/10

Abstract: A cross-modality processing method is related to a field of natural language processing technologies. The method includes: obtaining a sample set, wherein the sample set includes a plurality of corpus and a plurality of images; generating a plurality of training samples according to the sample set, in which each of the plurality of the training samples is a combination of at least one of the plurality of the corpus and at least one of the plurality of the images corresponding to the at least one of the plurality of the corpus; adopting the plurality of the training samples to train a semantic model, so that the semantic model learns semantic vectors containing combinations of the corpus and the images.