Sequence labeling method and device and training method of sequence labeling model

Abstract

The invention discloses a sequence labeling method and device, a training method and equipment of a sequence labeling model and a computer readable storage medium. In the scheme, a score layer of a sequence labeling model comprises second score layers which are in one-to-one correspondence with labeling specifications. The invention comprises a first score layer corresponding to all the labeling specifications. Due to the unique design of the score layer in the model, heterogeneous data of multiple annotation specifications can be used as a training set of the model, the scale of training corpora is expanded, and the model can learn the generality of corpora of different annotation specifications, so that the annotation performance of the model under a single annotation specification is improved. Besides, the output result of the model is a binding label sequence, equivalently, the label sequence under various labeling specifications is directly obtained, and conversion of the text between different labeling specifications is facilitated.

Description

technical field [0001] The present application relates to the field of natural language processing, and in particular to a sequence tagging method and device, a sequence tagging model training method, equipment, and a computer-readable storage medium. Background technique [0002] In natural language processing tasks, it is often necessary to use labeled data as training samples for natural language processing models, where the scale of labeled data significantly affects the performance of the model. Since the construction cost of manually labeled data is very expensive, some scholars have proposed a solution to expand the scale of data by using heterogeneous data resources. However, since heterogeneous data follow different annotation specifications, it is impossible to directly mix heterogeneous data. Therefore, how to effectively use heterogeneous data to improve model performance has become a research problem. [0003] At present, a solution to improve model performanc...

AI Technical Summary

Problems solved by technology

[0005] The purpose of this application is to provide a sequence labeling method, device, training method, equipment and computer-readable storage medium of a sequence labeling model to solve the existing The solution of using data with different labeling specifications to train the model cannot achieve the problem of improving the performance of model labeling

Images