US 12,236,361 B2
Question analysis method, device, knowledge base question answering system and electronic equipment
Wenbin Jiang, Beijing (CN); Huanyu Zhou, Beijing (CN); Meng Tian, Beijing (CN); Ying Li, Beijing (CN); Xinwei Feng, Beijing (CN); Xunchao Song, Beijing (CN); Pengcheng Yuan, Beijing (CN); Yajuan Lyu, 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 Sep. 29, 2020, as Appl. No. 17/037,612.
Claims priority of application No. 202010267909.9 (CN), filed on Apr. 8, 2020.
Prior Publication US 2021/0319335 A1, Oct. 14, 2021
Int. Cl. G06F 40/30 (2020.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01)
CPC G06N 5/04 (2013.01) [G06F 40/30 (2020.01); G06N 5/02 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A question analysis method, comprising:
analyzing a question to obtain N linearized sequences, N being an integer greater than 1;
converting the N linearized sequences into N network topology maps;
separately calculating a semantic matching degree of each of the N network topology maps to the question; and
selecting a network topology map having a highest semantic matching degree to the question as a query graph of the question from the N network topology maps;
wherein the separately calculating the semantic matching degree of each of the N network topology maps to the question comprises:
acquiring a semantic representation vector of the question;
acquiring a semantic representation vector of each of the N network topology maps; and
separately calculating the semantic matching degree of each of the N network topology maps to the question according to the semantic representation vector of the question and the semantic representation vector of each of the N network topology maps;
wherein the method further comprises:
in the acquiring the semantic representation vector of the question and the semantic representation vector of the network topology map, exchanging information between the question and the network topology map based on an attention mechanism, to generate the semantic representation vector of the question and the semantic representation vector of the network topology map.