Entity recognition model training method and device, entity recognition method and device, equipment and medium

Abstract

The invention discloses an entity recognition model training method and device, computer equipment and a storage medium, and the method comprises the steps: firstly obtaining a sample data set, enabling each piece of sample data in the sample data set to comprise N pieces of labeling data, and enabling N to be a positive integer; according to the sample data set, training a preset multi-layer recognition model to obtain an entity recognition model, wherein the multi-layer recognition model comprises a main model and N entity sub-models, and each piece of annotation data of each piece of sampledata corresponds to one entity sub-model. According to the method, a plurality of annotation data are set in the sample data, and when the multi-layer recognition model is trained, the network structure of the main model and the N entity sub-models is set, so that the memory consumption during training can be reduced. Moreover, N pieces of annotation data are set for one piece of sample data, sothat the recognition precision of the model can be better ensured on the premise of not reducing the number of the sample data. The invention further discloses an entity identification method and device, computer equipment and a storage medium.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to an entity recognition model training method, entity recognition method, device, equipment and medium. Background technique [0002] With the continuous development of computer technology, Natural Language Processing (Natural Language Processing, NLP) technology is becoming more and more mature, and there are more and more technical applications for semantic recognition in NLP. For example, voice assistants, voice intelligent robots and voice retrieval, etc. Semantic recognition is generally divided into entity recognition and intent recognition. Entity recognition is usually achieved by training one or more entity recognition models. However, there may be a problem of recognition accuracy in entity recognition through a single entity recognition model, and if multiple entity models are used to identify, it will inevitably occupy too much memory resou...

AI Technical Summary

Problems solved by technology

[0003] An embodiment of the present invention provides an entity recognition model training method, device, computer equipment and storage medium to solve the problem that entity recognition model training occupies too much memory resources and increases the burden on the computer

[0004] An embodiment of the present invention provides an entity recognition method, device, computer equipment, and storage medium to solve the problem of low recognition accuracy of entity recognition models

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