Abstract: Embodiments of the present application disclose a natural language processing method and apparatus, a device, and a readable storage medium. The method includes: obtaining a target sentence to be processed, and determining each first entity in the target sentence; for each first entity, in response to the first entity being present in a preset entity set, determining, in the preset entity set, a second entity in maximum correlation with the first entity, generating extended information based on the determined second entity, and adding the extended information after a location of the first entity in the target sentence, to obtain an updated target sentence, where the second entity is any entity in the preset entity set other than the first entity; and inputting the updated target sentence to a bidirectional encoder representations from transformer (BERT) model, such that the BERT model performs a natural language processing task.

Abstract: This disclosure relates to a context enforcement system that efficiently and securely protects tenant context information that travels across microservices in a multi-tenant distributed cloud computing system and protects against data leaks that often occur in conventional microservice management systems. For example, the context enforcement system ensures secure external and internal communications and context isolation by providing various shared library functions to microservices of a multi-tenant distributed cloud computing system. Additionally, the shared library provided by the context enforcement system improves the efficiency of the multi-tenant distributed cloud computing system by allowing microservices to focus on target operations rather than also maintaining and performing additional redundant functions.

Abstract: Embodiments of the present application disclose a natural language processing method and apparatus, a device, and a readable storage medium. The method includes: obtaining a target sentence to be processed, and determining each first entity in the target sentence; for each first entity, in response to the first entity being present in a preset entity set, determining, in the preset entity set, a second entity in maximum correlation with the first entity, generating extended information based on the determined second entity, and adding the extended information after a location of the first entity in the target sentence, to obtain an updated target sentence, where the second entity is any entity in the preset entity set other than the first entity; and inputting the updated target sentence to a bidirectional encoder representations from transformer (BERT) model, such that the BERT model performs a natural language processing task.