Entity relationship processing method and device in text, electronic equipment and storage medium

Abstract

The invention provides an entity relationship processing method and apparatus in a text, an electronic device and a storage medium. The method comprises the steps of obtaining a target text; inputtingthe target text into a preset entity relationship processing model to obtain the category of the entity relationship in the target text; wherein the entity relationship processing model is a model which is obtained through machine learning training by taking the feature information of the training text and the category of the entity relationship as input and is used for determining the category of the entity relationship in the text; wherein the entity relationship processing model comprises a coding layer, a parameter optimization layer and a classification layer, the coding layer is used for determining feature information according to the target text, the classification layer is used for determining the category of an entity relationship in the target text according to the feature information and classification parameters of the target text, and the parameter optimization layer is used for optimizing the classification parameters; the classification parameters are determined by themeta-information of the category of the text, so that the meta-information of the category is used as the judgment guidance of the characteristic information of the text, and the classification of the entity relationship in the text is more accurate and efficient.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a method, device, electronic equipment and storage medium for processing entity relationships in text. Background technique [0002] In the information age, massive amounts of data are generated every day. Represent proper nouns and things in the data such as person names, place names, time, and dates as entities. Extract the relationship between entities and entities from the data, and build a knowledge graph. The knowledge graph includes the category of the entity relationship and the relationship between entities. Therefore, after extracting the entity relationship from the new text, the category of the entity relationship is also determined to be stored in the knowledge graph. [0003] The extraction and classification of entity relations in text requires a model trained based on samples (ie, sample instances), but most of the model training is based ...

AI Technical Summary

Problems solved by technology

However, when there is sample feature instability (for example, the sample is noise data), or when the sample data is too small to support training and learning, the accuracy of the trained model cannot be guaranteed, and it will also cause the subsequent model to text The result of processing is not stable

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