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

A technology of convolutional network and relational extraction, which is applied in the field of relational extraction method and system based on attention cycle gated graph convolutional network, can solve the problems of key information loss and underutilization of dependency tree, and reduce redundancy The influence of features, the performance of relation extraction, and the effect of avoiding the loss of key information

Pending Publication Date: 2020-11-24
JIANGNAN UNIV
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Problems solved by technology

[0006] For this reason, the technical problem to be solved by the present invention is to overcome the problem that the dependency tree is not fully utilized and the key information will be lost in the prior art, so as to provide a method that makes full use of the dependency tree and fully extracts the features in the dependency tree, avoiding the key Method and system for relationship extraction based on attention loop gated graph convolutional network with information loss

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

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Embodiment 1

[0023] Such as figure 1 with figure 2 As shown, this embodiment provides a method for extracting relationships based on attention loop gated graph convolutional network, including the following steps: Step S1: Perform semantic dependency analysis on sentences, and construct a unique dependency tree for each input sentence, Use the pre-trained word vector to obtain the word embedding representation, connect the word embedding and the position feature to obtain the final word embedding representation; step S2: construct the BLSTM network layer, set the hyperparameter values ​​of the BLSTM network structure, and convert the final The word embedding representation of the word is input into the BLSTM network, and the word context feature vector is extracted; step S3: apply the attention mechanism to the dependency tree, convert the dependency tree into a fully connected graph, and obtain a fully connected graph with weight information The soft adjacency matrix of the graph; step ...

Embodiment 2

[0077] Based on the same inventive concept, this embodiment provides a relationship extraction system based on the attention cycle gated graph convolutional network, and its problem-solving principle is similar to the relationship extraction method based on the attention cycle gated graph convolutional network , the repetitions will not be repeated.

[0078] This embodiment provides a relationship extraction system based on attention loop gated graph convolutional network, including:

[0079] The semantic dependency analysis module is used to perform semantic dependency analysis on sentences, construct a unique dependency tree for each input sentence, use pre-trained word vectors to obtain word embedding representations, and connect word embeddings with positional features to obtain the final word embedded representation;

[0080] Construct network module, be used for constructing BLSTM network layer, set the hyperparameter value of BLSTM network structure, described final wo...

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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<...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/30G06N3/04G06N3/08
CPCG06F40/30G06N3/049G06N3/084G06N3/047G06N3/048G06N3/044G06N3/045
Inventor 钱雪忠王晓霞
Owner JIANGNAN UNIV
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