Method, apparatus, device, and storage medium for linking entity

1. A method for linking an entity, the method comprising:

acquiring a target text;

determining at least one entity mention comprised in the target text and a candidate entity corresponding to each entity mention;

determining an embedding vector of each candidate entity, based on the each candidate entity and a preset entity embedding vector determination model;

determining context semantic information of the target text, based on the target text and each embedding vector;

determining type information of the at least one entity mention based on a context vocabulary of each entity mention; and

determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.

2. The method according to claim 1 , wherein the entity embedding vector determination model comprises a first vector determination model and a second vector determination model, the first vector determination model representing a corresponding relationship between a description text of an entity and an embedding vector, and the second vector determination model representing a corresponding relationship between relationship information between entities and an embedding vector.

3. The method according to claim 2 , wherein the determining the embedding vector of the each candidate entity based on the each candidate entity and the preset entity embedding vector determination model, comprises:

acquiring a description text of the each candidate entity;

determining a first embedding vector of the each candidate entity based on each description text and the first vector determination model;

determining relationship information between candidate entities;

determining a second embedding vector of the each entity mention, based on the relationship information between candidate entities and the second vector determination model; and

determining the embedding vector of the each candidate entity, based on the first embedding vector and the second embedding vector.

4. The method according to claim 1 , wherein the determining context semantic information of the target text based on the target text and each embedding vector, comprises:

determining a word vector sequence of the target text; and

determining the context semantic information, based on the word vector sequence and the each embedding vector.

5. The method according to claim 4 , wherein the determining the word vector sequence of the target text, comprises:

determining an embedding vector of a candidate entity corresponding to the entity linking result, in response to acquiring the entity linking result of the at least one entity mention; and

updating the word vector sequence using the determined embedding vector.

6. The method according to claim 1 , wherein the determining type information of the at least one entity mention, comprises:

for each entity mention, occluding the entity mention in the target text; and

determining the type information of the entity mention, based on the occluded target text and a pre-trained language model.

7. The method according to claim 1 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

determining the candidate entity corresponding to the each entity mention, based on the each embedding vector, the context semantic information, the each type information, and a preset learning to rank model, and using the determined candidate entity as the entity linking result of the at least one entity mention.

8. The method according to claim 1 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

for each entity mention, determining a similarity between the entity mention and the each candidate entity, based on the context semantic information, an embedding vector of the entity mention, the type information of the entity mention, and a vector of the each candidate entity corresponding to the entity mention; and

determining a candidate entity having a highest similarity as the entity linking result of the entity mention.

9. The method according to claim 1 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

for each entity mention, determining the entity linking result of the entity mention, based on the context semantic information and the embedding vector of the entity mention; and

verifying the entity linking result using the type information of the entity mention.

10. An electronic device for linking an entity, comprising:

at least one processor; and

a memory, communicatively connected with the at least one processor;

the memory storing instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the at least one processor to perform operations, the operations comprising:

acquiring a target text;

determining at least one entity mention comprised in the target text and a candidate entity corresponding to each entity mention;

determining an embedding vector of each candidate entity, based on the each candidate entity and a preset entity embedding vector determination model;

determining context semantic information of the target text, based on the target text and each embedding vector;

determining type information of the at least one entity mention based on a context vocabulary of each entity mention; and

determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.

11. The electronic device according to claim 10 , wherein the entity embedding vector determination model comprises a first vector determination model and a second vector determination model, the first vector determination model representing a corresponding relationship between a description text of an entity and an embedding vector, and the second vector determination model representing a corresponding relationship between relationship information between entities and an embedding vector.

12. The electronic device according to claim 11 , wherein the determining the embedding vector of the each candidate entity based on the each candidate entity and the preset entity embedding vector determination model, comprises:

acquiring a description text of the each candidate entity;

determining a first embedding vector of the each candidate entity based on each description text and the first vector determination model;

determining relationship information between candidate entities;

determining a second embedding vector of the each entity mention, based on the relationship information between candidate entities and the second vector determination model; and

determining the embedding vector of the each candidate entity, based on the first embedding vector and the second embedding vector.

13. The electronic device according to claim 10 , wherein the determining context semantic information of the target text based on the target text and each embedding vector, comprises:

determining a word vector sequence of the target text; and

determining the context semantic information, based on the word vector sequence and the each embedding vector.

14. The electronic device according to claim 13 , wherein the determining the word vector sequence of the target text, comprises:

determining an embedding vector of a candidate entity corresponding to the entity linking result, in response to acquiring the entity linking result of the at least one entity mention; and

updating the word vector sequence using the determined embedding vector.

15. The electronic device according to claim 10 , wherein the determining type information of the at least one entity mention, comprises:

for each entity mention, occluding the entity mention in the target text; and

determining the type information of the entity mention, based on the occluded target text and a pre-trained language model.

16. The electronic device according to claim 10 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

determining the candidate entity corresponding to the each entity mention, based on the each embedding vector, the context semantic information, the each type information, and a preset learning to rank model, and using the determined candidate entity as the entity linking result of the at least one entity mention.

17. The electronic device according to claim 10 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

for each entity mention, determining a similarity between the entity mention and the each candidate entity, based on the context semantic information, an embedding vector of the entity mention, the type information of the entity mention, and a vector of the each candidate entity corresponding to the entity mention; and

determining a candidate entity having a highest similarity as the entity linking result of the entity mention.

18. The electronic device according to claim 10 , wherein the determining the entity linking result of the at least one entity mention, based on each embedding vector, the context semantic information, and each type information, comprises:

for each entity mention, determining the entity linking result of the entity mention, based on the context semantic information and the embedding vector of the entity mention; and

verifying the entity linking result using the type information of the entity mention.

19. A non-transitory computer readable storage medium, storing computer instructions, the computer instructions, when executed by a computer, cause the computer to perform operations comprising:

acquiring a target text;

determining at least one entity mention comprised in the target text and a candidate entity corresponding to each entity mention;

determining an embedding vector of each candidate entity, based on the each candidate entity and a preset entity embedding vector determination model;

determining context semantic information of the target text, based on the target text and each embedding vector;

determining type information of the at least one entity mention based on a context vocabulary of each entity mention; and

determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.