Multi-class entity recognition model training method, entity recognition method, server and terminal

Abstract

The invention discloses a multi-class entity recognition model training method, an entity recognition method, a server and a terminal. The multi-class entity recognition model training method comprises the steps: carrying out the entity and entity class labeling of corpus information, and obtaining the target annotated corpus information comprising an entity and an entity class label; performing multi-dimensional feature analysis processing on the corpus information in the target annotated corpus information to obtain multi-dimensional information of the target annotated corpus information; performing multi-class entity recognition training on the preset deep learning model based on the multi-dimensional information and entities and entity class tags in the target annotation corpus information to obtain a multi-class entity recognition model, wherein the preset deep learning model comprises a feature input conversion layer, a semantic sequence representation layer, an entity feature screening layer and a class entity output layer. By utilizing the technical scheme provided by the invention, the entities and the entity categories in the corpus information can be quickly and accurately identified, and multi-category entity identification is realized.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a multi-category entity recognition model training, entity recognition method, server and terminal. Background technique [0002] With the development of artificial intelligence and big data technology, the technical demand for natural language processing continues to increase. Among them, named entity recognition, as a necessary pre-operation for tasks such as semantic understanding and speech synthesis, plays an important role in natural language understanding. [0003] Among the existing named entity (hereinafter referred to as entity) recognition methods, the entity recognition method based on conditional random field model is widely used. This method can train the model based on the training data of a certain field, and can combine the context information in the text to assist the recognition of entities in this field during the training process...

AI Technical Summary

Problems solved by technology

However, the effect of entity recognition in the above existing schemes depends entirely on the selection and design of training data, and cannot be applied to different fields.

In practical applications, it is often necessary to identify different types of entities in multiple fields including person names, place names, organization names, videos, cars, games, etc., resulting in low accuracy of entity recognition in practical applications.

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