Entity relationship classification model construction method and device and storage medium

Abstract

The invention relates to the field of data processing, and discloses an entity relationship classification model construction method and device and a storage medium, and the method comprises the steps: obtaining web text data; performing word segmentation and dependency syntactic analysis on text sentences, and extracting preprocessing of a plurality of features; enabling the training word vector vocabulary to input a plurality of features into an Embedding layer for training, and mapping the features into low-dimensional vector representation; inputting the low-dimensional vector representation of the sentence into a Bi-GRU layer for training; carrying out multiple times of self-attention calculation on all moment output vectors trained by the Bi-GRU layer by utilizing a Multi-headattention layer to obtain features of more layers in different representation subspaces, so as to obtain more context information of sentences; and extracting text information and position information of keywords in the entity relationship classification task through the capsule network layer, and classifying the entity relationship through the length of a capsule vector. According to the method, entity and position information can be utilized more effectively through the entity relationship classification model provided by combining Multi-headtention and a capsule network, the relationship classification effect is improved, and the position information sense is improved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a model building method, equipment and storage medium for entity relationship classification. Background technique [0002] Relation classification is an important task in the field of natural language processing, which can provide technical support in building knowledge graphs, question answering systems, and information retrieval. Most of the previous models use a single-layer attention mechanism, the feature expression is relatively single, and the feature expression ability is low. Therefore, in order to solve the problem of single feature expression, the present invention conducts further research and development on the existing basis, and introduces a method based on autonomous attention. The multi-head attention mechanism of the mechanism learns multiple mappers, and then obtains more levels of information from different dimensions and different subspaces to express features...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of relatively single feature expression and low feature expression ability in the prior art relationship classification, the present invention provides a model construction method, equipment and storage medium for entity relationship classification

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