Nested named entity recognition method and system, electronic equipment and readable medium

Abstract

The invention provides a nested named entity recognition method and system, electronic equipment and a readable medium. The nested named entity recognition method includes the steps: marking all textsin a corpus based on a preset text marking method to obtain a mark set, wherein the mark set comprises texts and corresponding named entities, and at least one text corresponds to multiple named entities; based on a preset clustering method, clustering the mark set according to each named entity to obtain a cluster set, the cluster set comprising a text and a named entity uniquely corresponding to the text; and based on a preset named entity recognition model with adaptive data enhancement, respectively identifying named entities in each cluster set. A nested named entity recognition problemis converted into a non-nested named entity recognition problem, so that the influence of named entity nesting on the recognition effect is reduced; the data enhancement degree is gradually improved according to the training effect; the data enhancement use intensity is controlled at the optimal level; and the training effect is improved so as to adapt to the nested named entity recognition task under the condition of insufficient samples.

Description

technical field [0001] The invention belongs to the technical field of named entity recognition, and in particular relates to a nested named entity recognition method, a nested named entity recognition system, an electronic device and a computer-readable storage medium. Background technique [0002] Named entity recognition (NER, Name Entity Recognition) is one of the basic research contents of natural language processing, and its task is to identify language blocks in text. Named entity recognition often faces the problems of named entity nesting and insufficient training samples in practical applications. [0003] The increase in the nesting of named entities makes it impossible to establish a one-to-one relationship between text and entity labels. For example, "Bethune Medical College" is an organization name entity, while "Bethune" is a person name entity. Therefore, in the text labeling process, "Bethune" There are two tabs. The multi-label problem will increase the c...

AI Technical Summary

Problems solved by technology

The construction of training sample datasets for named entity recognition in professional fields is a time-consuming process that requires people with professional knowledge to annotate the data, so it is difficult to form large datasets

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