Graph convolutional network relationship extraction method based on multi-dependency relationship representation mechanism

Abstract

The invention provides a graph convolutional network relationship extraction method based on a multi-dependency relationship representation mechanism, and the method comprises the following steps: carrying out preprocessing on a collected unstructured text, including sentence segmentation, word segmentation, part-of-speech tagging, entity type tagging, relationship type annotation and generation of a semantic embedding vector of each segmented word, performing dependency relationship analysis on sentences, and generating a dependency relationship tree; capturing context semantic features of sentences based on a bidirectional long-short-term memory recurrent neural network; generating a full adjacency matrix, a concentrated adjacency matrix and a distance weight adjacency matrix according to the dependency relationship tree, performing convolution operation on the adjacency matrix, the concentrated adjacency matrix and the distance weight adjacency matrix in combination with context semantic features of the sentence, and performing maximum pooling processing on a result after the convolution operation to obtain a sentence representation vector; obtaining the entity relationship feature information based on the feedforward neural network, and carrying out the entity relationship classification. According to the method, relation extraction can be better assisted, and the recognition precision is improved.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to a graph convolution network relation extraction method based on a multi-dependency relation representation mechanism. Background technique [0002] In the era of big data, Internet information is increasing rapidly. How to effectively mine high-quality and structured knowledge information from massive unstructured texts is a difficult point in the research of natural language processing technology. Relation extraction is an important part of information extraction, and its purpose is to classify the semantic relationship between entities in the text. Relation classification is divided into supervised classification, unsupervised classification, semi-supervised classification and open domain classification. At present, the supervised extraction method based on deep neural network is the mainstream of relation extraction. [0003] Deep neural networks can learn the sema...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a graph convolutional network relationship extraction method based on a multi-dependency representation mechanism to solve the problem that the existing graph convolutional network-based relationship extraction model needs to rely on a predefined method to extract relationship keywords, resulting in model failure. Poor sentence compatibility, inflexible screening methods, and easy to ignore the problems of relational indicators scattered on non-trunk paths of sentences

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