Nested entity data identification method and device, and electronic equipment

Abstract

The invention discloses a nested entity data identification method and device, and electronice equipment, and relates to the technical field of data identification. The method comprises the steps of permutating and combining seed entity vocabularies of different entity categories to generate a short text data set; defining at least one entity category label for a short text in the short text dataset, and index information of starting and ending of a sub-text, corresponding to each entity category label, in the short text; training a deep learning recognition model by using the defined short text data set as a training set; and recognizing the nested entity data by using the recognition model which is trained to reach the standard. According to the method, the entity labeling information is defined for the statement according to the start and end indexes and the entity category label, so that the multi-nested entity content labeling is simpler to implement, the labeling process and workload of nested entity recognition are optimized, the time cost and the labor cost are saved. And thus, the identification efficiency and accuracy of the nested entity data can be improved.

Description

technical field [0001] The present application relates to the technical field of data identification, in particular to an identification method, device and electronic equipment for nested entity data. Background technique [0002] Named Entity Recognition (NER) is an important research direction in the field of natural language processing. It refers to the recognition of entities with specific meaning in text, mainly including names of people, places, institutions, and proper nouns. With the development of deep learning technology and the needs of actual production applications, the requirements for named entity recognition are also increasing. When using entities for search support, fine-grained and nested entity information is required to ensure the accuracy and coverage of searches. At this stage, the main technology used for named entity recognition is deep learning technology. [0003] When using deep learning technology for entity recognition, a large amount of labele...

AI Technical Summary

Problems solved by technology

[0004] However, this type of entity data labeling method is difficult to divide the granularity of entities, and multi-level BIO labeling requires a lot of time and labor costs. It is difficult to process a large amount of effective labeling data in a short period of time, which will affect the identification of nested entity data. efficiency and accuracy

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