Method and apparatus for named entity recognition, and non-transitory computer-readable recording medium

An apparatus for named entity recognition, the apparatus comprising:

• a memory storing computer-executable instructions;
• and
• one or more processors configured to execute the computer-executable instructions such that the one or more processors are configured to
• determine at least one span in a sequence to be predicted, generate a vector representation of the sequence to be predicted using a pre-trained recognition model, and obtain a vector representation of the span in the sequence to be predicted, the number of tokens in the span being less than or equal to a preset threshold;
• and
• compare the vector representation of a first span in the sequence to be predicted with final representations of one or more entity type identifiers obtained in advance to determine an entity type of the first span, the first span being a span among the at least one span,
• wherein the recognition model is obtained by performing fine-tuning training on a pre-trained language model based on an objective function of contrastive learning, the objective function is constructed based on vector representations of spans in training samples, so that distances between the vector representations of the spans belonging to the same class are minimized and distances between the vector representations of the spans belonging to different classes are maximized, the training samples include a plurality of original samples and a plurality of enhanced samples, the original samples are samples with labeled entity types, the enhanced samples are obtained by replacing one or more entities in the original samples with respective entity type identifiers, and the final representations of the entity type identifiers are obtained by performing average pooling on the vector representations of the same entity type identifier among the vector representations of the plurality of enhanced samples generated by the recognition model.

A non-transitory computer-readable recording medium having computer-executable instructions for execution by one or more processors, wherein, the computer-executable instructions, when executed, cause the one or more processors to carry out a method for named entity recognition, the method comprising:

• determining at least one span in a sequence to be predicted, generating a vector representation of the sequence to be predicted using a pre-trained recognition model, and obtaining a vector representation of the span in the sequence to be predicted, the number of tokens in the span being less than or equal to a preset threshold;
• and
• comparing the vector representation of a first span in the sequence to be predicted with final representations of one or more entity type identifiers obtained in advance to determine an entity type of the first span, the first span being a span among the at least one span,
• wherein the recognition model is obtained by performing fine-tuning training on a pre-trained language model based on an objective function of contrastive learning, the objective function is constructed based on vector representations of spans in training samples, so that distances between the vector representations of the spans belonging to the same class are minimized and distances between the vector representations of the spans belonging to different classes are maximized, the training samples include a plurality of original samples and a plurality of enhanced samples, the original samples are samples with labeled entity types, the enhanced samples are obtained by replacing one or more entities in the original samples with respective entity type identifiers, and the final representations of the entity type identifiers are obtained by performing average pooling on the vector representations of the same entity type identifier among the vector representations of the plurality of enhanced samples generated by the recognition model.