公开(公告)号：US20220358292A1

公开(公告)日：2022-11-10

申请号：US17813691

申请日：2022-07-20

Applicant: Beijing Baidu Netcom Science Technology Co., LTD.

Inventor： Fan Wang ,  Jinchang Luo ,  Jie Wang ,  Haiwei Wang ,  Kunbin Chen ,  Wei He

IPC: G06F40/295 ,  G06F16/33 ,  G06F16/35 ,  G06F16/31

Abstract: The disclosure provides a method for recognizing an entity, an electronic device and a storage medium. The method includes: obtaining message data to be processed; obtaining entity mention information by processing the message data to be processed according to a multi-pattern matching method; determining one or more candidate entities associated with the entity mention information and entity description information corresponding to the one or more candidate entities; and determining a target entity mentioned in the entity mention information according to the message data to be processed and the entity description information.

公开(公告)号：US12174824B2

公开(公告)日：2024-12-24

申请号：US18147823

申请日：2022-12-29

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

Inventor： Wei Xu ,  Xiaoling Xia ,  Junxiang Jiang ,  Chengtai Cao ,  Bolei He ,  Kunbin Chen ,  Wei He

IPC: G06F16/23 ,  G06F9/451 ,  G06F16/906 ,  G06F18/10 ,  G06F18/213 ,  G06F18/241 ,  G06F18/2415

Abstract: A method for denoising click data includes: acquiring a set of click data including pieces of first click data and a real label corresponding to each piece of first click data; extracting feature vectors of each piece of first click data with a graph model; dividing the feature vectors into sets of feature vectors; obtaining trained binary classification models by training binary classification models with the sets of feature vectors; for each of the feature vectors, obtaining prediction values corresponding to the feature vector by predicting the feature vector with the trained binary classification models, and calculating a prediction label of the feature vector based on the prediction values of the feature vector; and removing noise data in the pieces of first click data, based on the pieces of first click data, the real label and the prediction label of each piece of first click data.