US 11,704,492 B2
Method, electronic device, and storage medium for entity linking by determining a linking probability based on splicing of embedding vectors of a target and a reference text
Qi Wang, Beijing (CN); Zhifan Feng, Beijing (CN); Zhijie Liu, Beijing (CN); Siqi Wang, Beijing (CN); Chunguang Chai, Beijing (CN); and Yong Zhu, Beijing (CN)
Assigned to Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing (CN)
Filed by Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing (CN)
Filed on Mar. 26, 2021, as Appl. No. 17/213,927.
Claims priority of application No. 202010326675.0 (CN), filed on Apr. 23, 2020.
Prior Publication US 2021/0216716 A1, Jul. 15, 2021
Int. Cl. G06F 40/295 (2020.01); G06F 16/36 (2019.01); G06Q 10/04 (2023.01); G06F 16/33 (2019.01); G06F 40/30 (2020.01)
CPC G06F 40/295 (2020.01) [G06F 16/3344 (2019.01); G06F 40/30 (2020.01)] 16 Claims
OG exemplary drawing
 
1. A method for entity linking, comprising:
acquiring a target text;
determining at least one entity mention included in the target text;
determining a candidate entity corresponding to each of the entity mention based on a preset knowledge base;
determining a reference text of each of the candidate entity and determining additional feature information of each of the candidate entity; and
determining an entity linking result based on the target text, each of the reference text, and each piece of the additional feature information,
wherein the determining the entity linking result based on the target text, each of the reference text, and each piece of the additional feature information comprises:
determining a first embedding vector of the target text, a second embedding vector of the target text, a first embedding vector of each of the reference text, and a second embedding vector of each of the reference text respectively;
splicing, for each reference text, the first embedding vector of the reference text, the second embedding vector of the reference text, and additional feature information of a candidate entity corresponding to the reference text, to obtain a first spliced vector;
splicing the first embedding vector of the target text, the second embedding vector of the target text, and each of the first spliced vector, to obtain a second spliced vector; and
determining a probability of linking each of the candidate entity to the entity mention based on each of the first spliced vector, the second spliced vector, and a preset classification model.