Named entity recognition model training method and device

Abstract

The invention provides a named entity recognition model training method and device. The named entity recognition model training method comprises the steps of obtaining labeled training data and unlabeled training data; training a target named entity recognition model according to the labeled training data; inputting the unlabeled training data into the target named entity recognition model to obtain at least one entity word output by the target named entity recognition model and a confidence score corresponding to each entity word; determining a target entity word according to the confidence score corresponding to each entity word, and labeling the unlabeled training data according to the target entity word to generate newly added labeled training data; training the target named entity recognition model contineusly according to the newly added labeled training data. According to the method, through a weak supervised learning mode, the number of the labeled training data is increased, model training overfitting is effectively prevented, and meanwhile the cost of manually labeling the labeled training data is reduced.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a named entity recognition model training method and device, a named entity recognition method and device, computing equipment, and a computer-readable storage medium. Background technique [0002] Named entity recognition (NER) is a basic task in natural language processing, which has a wide range of applications in many scenarios such as text information understanding, knowledge question answering, retrieval, and graph construction. [0003] NER refers to the extraction of entities with specific meaning or strong referentiality from the input text. At present, the main methods used are entity recognition methods based on rules and dictionaries, machine learning methods and deep learning methods. In some specific fields ( Such as finance, medical care, military, government affairs, etc.), especially in some emerging fields, it is often impossible to obtain a large am...

AI Technical Summary

Problems solved by technology

[0003] NER refers to the extraction of entities with specific meaning or strong referentiality from the input text. At present, the main methods used are entity recognition methods based on rules and dictionaries, machine learning methods and deep learning methods. In some specific fields ( Such as finance, medical care, military, government affairs, etc.), especially in some emerging fields, it is often impossible to obtain a large amount of labeled training data, so that the named entity recognition model cannot be directly trained. The way of labeling will cost a lot of manpower and material resources, and the cost is high

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