Abstract: Disclosed is a method and an apparatus NER-orientated Chinese clinical text data augmentation, and unannotated data and annotated data of label linearization processing through data preprocessing. A concealed part is predicted based on retained information by using the unannotated data and concealing part of information in text, and meanwhile an entity word-level discrimination task is introduced for pre-training of a span-based language model; and a plurality of decoding mechanisms are introduced in a fine-tune stage, a relationship between a text vector and text data is obtained based on the pre-trained span-based language model, linearized data with entity labels is converted into the text vector, and text generation is performed through forward decoding and reverse decoding in a prediction stage of a text generation model to obtain enhanced data with annotation information.

Abstract: An application-level memory control group of a first application may be created when the first application is opened. An anonymous page of the first application is added to a least recently used linked list of the application-level memory control group, and a file page of the first application is added to a global least recently used linked list. An application-level memory control group is created in a dimension of an application, and an anonymous page of the application is managed in a refined manner. In addition, a file page of the application-level memory control group may be managed based on a global least recently used linked list.