Entity relationship extraction method, entity relationship learning model acquisition method and equipment

Abstract

The invention discloses an entity relationship extraction method, an entity relationship learning model acquisition method and equipment. The method comprises: obtaining a target text and a target entity relationship learning model, wherein the target entity relationship learning model is obtained based on a prototype feature set corresponding to a target entity relationship set; calling a targetentity relationship learning model to obtain text features of the target text and target prototype features corresponding to the target entity relationships; determining the matching degree of the target text and any target entity relationship based on the text feature and the target prototype feature corresponding to any target entity relationship; and determining an entity relationship corresponding to the target text based on the matching degree of the target text and each target entity relationship. In this way, prototype features in the prototype feature set can represent the entity relationship more comprehensively, the target entity relationship learning model obtained on the basis of the prototype feature set has a good entity relationship learning effect, and the accuracy of entity relationship extraction by means of the target entity relationship learning model is high.

Description

technical field [0001] The embodiments of the present application relate to the technical field of artificial intelligence, and in particular to an entity relationship extraction method, a method and a device for acquiring an entity relationship learning model. Background technique [0002] Information extraction aims to extract structured information from large-scale unstructured or semi-structured natural language texts. Entity relationship extraction is one of the important subtasks in information extraction. The purpose of entity relationship extraction is to extract the entity relationship between entities from text. For example, extracting the entity relationship "as the chairman" from the given text "Newton is the chairman of the Royal Society", the extracted entity relationship can be used as an external resource for various downstream applications (such as search engines, question answering systems, etc.). With the development of artificial intelligence technology,...

AI Technical Summary

Problems solved by technology

The representativeness of the memory text corresponding to the entity relationship is poor, and it is difficult to fully represent the entity relationship. The entity relationship learning model based on the entity relationship corresponding to the memory text has a poor entity relationship learning effect. Based on the obtained entity relationship learning model, the entity The accuracy of relation extraction is low

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