Relation extraction method and system based on attention cycle gated graph convolutional network

Abstract

The invention relates to a relation extraction method and system based on an attention cycle gated graph convolutional network, and the method comprises the steps of carrying out the semantic dependency analysis of a statement, enabling word embedding to be connected with a position feature, and obtaining a final word embedding representation; constructing a BLSTM network layer, and extracting a word context feature vector; applying an attention mechanism to the dependency tree to obtain a soft adjacency matrix of a fully connected graph with weight information; transmitting the word context feature vector and the soft adjacency matrix into a gated graph convolutional network, and extracting a high-order semantic dependence feature to obtain vector representation of a statement; and extracting vector representations of the two marked entities, splicing the extracted vector representations of the two marked entities with the vector representation of the statement, transmitting the spliced vector representation of the statement into a full connection layer of the gated graph convolutional network, calculating the probability of each relationship type and predicting the relationship type, and finally obtaining the relationship type of the statement. According to the invention, key information loss is avoided, and the relationship extraction performance is improved.

Description

technical field

[0001] The present invention relates to the technical field of natural language processing relation extraction, in particular to a relation extraction method and system based on Attention Recurrent Gating Graph Convolutional Network (Att-RGate-GCN for short). Background technique

[0002] Relation extraction is an important subtask in the field of natural language processing, and it is the cornerstone of large-scale relational understanding applications for unstructured text. It has a wide range of applications in information extraction, question answering systems, and knowledge graphs. With the advent of the era of big data, the ability to deal with explosive data is getting higher and higher, and it is more and more important to correctly understand the relationships existing in sentences. Relation extraction is to identify the semantic relationship between two entities in the text according to the predefined relationship types. For example, "The train<...

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