Unified low sample relation extraction method and device based on multi-choice matching network

Abstract

The invention discloses a unified low sample relation extraction method and device based on a multi-choice matching network. The method comprises the following steps: based on a pre-training language model and a multi-choice marked relationship description and relationship instance common coding and matching mechanism; the triple is obtained through extraction based on open information of a large-scale plain text, the retelling text is generated through a generative pre-training language model, and a triple-retelling pre-training mode is carried out based on the retelling text; the invention relates to an online element learning training mode based on small samples under a new task. According to the mechanism based on the multi-choice matching network, various scenes in a low-sample relation extraction task can be modeled in a unified manner, and an efficient and rapid network architecture is provided, so that the model better meets multiple requirements on model performance and speed in practical application.

Description

technical field [0001] The invention relates to a low-sample relationship extraction method, in particular to a low-sample relationship extraction method and device based on a multi-selection matching network and its pre-training and online training methods, belonging to the technical field of natural language processing. Background technique [0002] Relation extraction is an important task in information extraction and natural language understanding. The task aims to identify the class of relationship expressed by a pair of entities, given the context. E.g. Given the context "A is the founder of Company B", and the entity pair "A" and "Company B", a relation extraction system should be able to correctly identify the entity pair expressing the relation "is the founder of...". [0003] In recent years, with the development of deep learning methods, the accuracy of relation extraction systems has been greatly improved. However, such methods require a large amount of high-q...

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Problems solved by technology

[0007] In order to overcome the problem of high computational cost in the existing unified low-sample relation extraction method, the present invention proposes a unified relation extraction method and device based on a multi-choice matching network, and models the relation extraction as a multi-choice matching task that is very convenient for calculation. And use the task-specific pre-training method to perform specific optimization and training on the multi-choice matching network, thus avoiding the problem of high computational cost of the model

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