Method and system for understanding medical chinese spoken language, electronic device, and storage medium

A system for understanding a medical Chinese spoken language, comprising:

• a spoken language obtaining module configured to obtain a to-be-understood medical Chinese spoken language;
• a conversion module configured to convert the to-be-understood medical Chinese spoken language into Chinese text data to obtain a to-be-understood medical sentence;
• a first question sentence constructing module configured to construct an intent recognition question sentence for the to-be-understood medical sentence;
• a first vector matrix determining module configured to input the to-be-understood medical sentence into a first semantic vector determining model to obtain a medical identification vector semantic representation matrix, wherein the medical identification vector semantic representation matrix comprises X medical identification vector semantic representations, an xth medical identification vector semantic representation is a vector semantic representation of an xth medical identification character, and the xth medical identification character is an xth character in the to-be-understood medical sentence;
• and X>1, and 1≤x≤X;
• a second vector matrix determining module configured to input the intent recognition question sentence into the first semantic vector determining model to obtain an intent recognition vector semantic representation matrix, wherein the intent recognition vector semantic representation matrix comprises m1 intent recognition vector semantic representations, an i1th intent recognition vector semantic representation is a vector semantic representation of an i1th intent recognition character, and the i1th intent recognition character is an i1th character in the intent recognition question sentence;
• and m1>1, and 1≤i1≤m1;
• a first weighting module configured to determine a first weighted vector matrix of the to-be-understood medical sentence based on the medical identification vector semantic representation matrix and the intent recognition vector semantic representation matrix;
• a first concatenation module configured to perform concatenation and a linear change on the first weighted vector matrix and the medical identification vector semantic representation matrix of the to-be-understood medical sentence to obtain a concatenated first vector of the to-be-understood medical sentence;
• an intent recognition module configured to input the concatenated first vector of the to-be-understood medical sentence into an intent recognition model to obtain an intent recognition prediction value of the to-be-understood medical Chinese spoken language;
• a second question sentence constructing module configured to construct a slot filling question sentence for the to-be-understood medical Chinese spoken language based on the intent recognition prediction value of the to-be-understood medical Chinese spoken language;
• a third vector matrix determining module configured to input the slot filling question sentence into a second semantic vector determining model to obtain a slot filling vector semantic representation matrix, wherein the slot filling vector semantic representation matrix comprises m2 slot filling vector semantic representations, an i2th slot filling vector semantic representation is a vector semantic representation of an i2th slot filling character, and the i2th slot filling character is an i2th character in the slot filling question sentence;
• and m2>1, and 1≤12≤m2;
• a second weighting module configured to determine a second weighted vector matrix of the to-be-understood medical sentence based on the medical identification vector semantic representation matrix and the slot filling vector semantic representation matrix;
• a second concatenation module configured to perform concatenation and a linear change on the second weighted vector matrix and the medical identification vector semantic representation matrix of the to-be-understood medical sentence to obtain a concatenated second vector of the to-be-understood medical sentence;
• and
• a slot filling module configured to input the concatenated second vector of the to-be-understood medical sentence into a slot filling model to obtain a slot filling prediction value of the to-be-understood medical Chinese spoken language;
• wherein the first semantic vector determining model, the intent recognition model, the second semantic vector determining model, and the slot filling model are determined through training by using multiple training medical Chinese spoken languages, corresponding real intent labels, and real slot filling labels.