Combinable weak authenticator-based named entity identification algorithm architecture

Abstract

The invention discloses a combinable weak authenticator-based named entity identification algorithm architecture. The architecture comprises an entity identification part and a result authentication part, the entity identification part is used for completing an identification task to obtain an identification result; and the result authentication part comprises two or more weak authenticators which are respectively used for checking and authenticating the identification result on the subdivision target corresponding to each weak authenticator. The weak authenticator is a module capable of independently completing a subdivision target, and required training data can be automatically generated on an existing task corpus. The weak authenticator and the entity identification part form an end-to-end network and are used for carrying out optimization learning by using a supervision method. According to the invention, the combinable weak authenticator is used for assisting the named entity identification process, so that the entity identification precision is effectively improved, and the method can be simply and quickly adapted and expanded in an entity identification scene in a specific field.

Description

technical field [0001] The invention relates to a named entity recognition method and device based on a combinable weak authenticator, and belongs to the technical field of named entity recognition. Background technique [0002] Named entity recognition refers to the process of locating entity boundaries in text and classifying entities based on a predefined set of entity types. Named entity recognition results provide support for many downstream tasks such as knowledge graph construction, relation extraction, and information retrieval. Early named entity recognition mainly recognized simple entities such as names of people, places, and organizations. With the continuous expansion of the application field of named entity recognition, the types of entities have gradually increased, and there are some domain-specific entities in special fields. Type, such as the drug name in the biomedical field, etc. [0003] Named entity recognition can be subdivided into at least two proc...

AI Technical Summary

Problems solved by technology

In the prior art, when any one of the above two links makes a mistake, the final named entity recognition error will be caused: the current named entity recognition method usually regards the above two processes as one (traditional named entity recognition methods usually use the named entity Recognition is regarded as a sequence labeling task, using a deep network combined with a conditional random field for entity labeling, that is, combining entity boundary recognition and entity type recognition into one task), or processing one of the processes separately, resulting in the learning process, I do not know which one Links lead to the failure of the final entity recognition

[0004] The naming of entities can be freely defined according to the needs in different fields without strict constraints, strong subjective consciousness, and large differences in the boundaries of entities, making named entity recognition more difficult

Fields that require entity recognition lack a large number of labeled named entity recognition data sets, which seriously affects the effect of supervised learning methods. Therefore, how to use the limited entity labeling data in the field to learn more effectively is the key to achieving good results in named entity recognition. The essential

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