Natural language labeling method based on artificial intelligence and related equipment

Abstract

The invention relates to the field of artificial intelligence, and discloses a natural language labeling method based on artificial intelligence and related equipment. The method comprises the steps of obtaining a to-be-labeled natural language text; through a pre-trained sequence labeling model, carrying out semantic coarse-grained sequence labeling on the sequence labeling model to obtain a labeling sequence; determining a target word in a natural language text according to the labeling sequence, and determining a target role type of the target word; obtaining a preset template word corresponding to the target role type, and calculating a similarity value between the preset template word and the target role type; and according to the similarity value and a preset sub-category judgment rule, determining a sub-category corresponding to the target word as a target sub-category, and performing semantic fine-grained sequence labeling on the natural language text to obtain a labeled text.In addition, the invention further relates to a blockchain technology, and the natural language text to be labeled and / or the labeled text can be stored in the blockchain. According to the method, thecorpus annotation efficiency for language model training can be improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an artificial intelligence-based natural language tagging method and related equipment. Background technique [0002] With the vigorous development of deep learning technology, there are also in-depth applications in natural language processing. The trained model can perform word segmentation, understanding, and even emotion classification of natural language to understand the intent of the sentence. The training of the model requires a large number of labeled sentence samples. Currently, two types of deep learning models are mainly used for semantic understanding. One is semantic understanding with supervised learning, and the other is semantic understanding that combines semi-supervised learning, rule writing, and pre-trained word vectors. However, both the former and the latter require a large number of labeled sentences. In terms of intent recognition, the higher th...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to solve the problem that the training corpus cannot be marked quickly at present

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