Abstract: The present disclosure provides a method for selecting an annotated sample. The method includes: determining a first attribute and a second attribute of a sample characteristic; in which the first attribute is a characteristic attribute of the sample characteristic in a source field sample set, and the second attribute is a characteristic attribute of the sample characteristic in a target field sample set; and determining a target annotated sample from a plurality of candidate annotated samples of the source field sample set according to the first attribute and the second attribute; in which the target annotated sample is configured to train a classification model, the classification model includes a model for determining an emotion polarity by analyzing an input sample to be classified.

Abstract: There is provided an entity linking method, an electronic device, and a storage medium, which relates to the technical field of artificial intelligence such as machine learning, natural language processing, and intelligent search. A specific implementation solution involves: acquiring a target entity in a knowledge base and most relevant to a to-be-linked entity in a specified statement; and deciding, based on a linking decision strategy, whether to link the to-be-linked entity to the target entity in the knowledge base.

Abstract: There is provided a sentiment parsing method and apparatus, an electronic device, and a storage medium, which relates to the technical field of artificial intelligence such as machine learning and natural language processing. A specific implementation solution involves: identifying a role of a sentiment parsing object in a specified statement; trimming the specified statement based on the role of the sentiment parsing object to acquire pruning result information after the trimming; and parsing sentiment of the sentiment parsing object in the specified statement based on the pruning result information.

Abstract: The present disclosure provides a method for selecting an annotated sample. The method includes: determining a first attribute and a second attribute of a sample characteristic; in which the first attribute is a characteristic attribute of the sample characteristic in a source field sample set, and the second attribute is a characteristic attribute of the sample characteristic in a target field sample set; and determining a target annotated sample from a plurality of candidate annotated samples of the source field sample set according to the first attribute and the second attribute; in which the target annotated sample is configured to train a classification model, the classification model includes a model for determining an emotion polarity by analyzing an input sample to be classified.