Text statement processing method and device, computer equipment and storage medium

Abstract

The invention relates to a text statement processing method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a sample text statement containing an entity pair and a relationship label of the entity pair; extracting a positive example statement pair and a negative example statement pair from the sample text statement according to the relationship label, and performing positive and negative example sampling processing to obtain a training set; inputting the training set into a to-be-trained relationship extraction model, and generating a loss valuecomprising a comparison loss value; wherein the comparison loss value is used for representing the difference between the similarity of the statements in the positive example statement pair and the similarity of the statements in the negative example statement pair; adjusting parameters of the relation extraction model according to the loss value, and returning to the step of extracting the positive example statement pair and the negative example statement pair from the sample text statement according to the relation label, so as to carry out iterative training until a training stop conditionis met, and obtaining the relation extraction model; wherein the relationship extraction model is used for identifying the entity relationship of the entity pair in the text statement. By adopting the method, the entity relationship extraction accuracy can be effectively improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a text statement processing method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of computer technology, natural language processing technology has gradually matured. Among them, relation extraction is an important natural language processing task, which aims to extract structured information from large-scale original texts for question answering systems, dialogue systems, recommendation systems, search engines, etc. With the development of neural network models, more and more relation extraction methods based on feature engineering have emerged. [0003] The current relationship extraction methods usually rely on feature engineering. Most relationship extraction models use the word vector of the entity as the main feature of relationship extraction. When performing relationship extraction, they will overfit ...

AI Technical Summary

Problems solved by technology

This is easy to cause error accumulation in the process of constructing features, hindering system performance, and resulting in low accuracy of entity relationship extraction in natural language processing tasks

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