Text mining method for heterogeneous graph conversion based on meta-structure learning

Abstract

The invention discloses a text mining method for heterogeneous graph conversion based on meta-structure learning, which comprises the following steps of: aiming at text data, extracting information in a text to construct a heterogeneous information network graph; acquiring a meta-path through a graph conversion layer to capture a relationship between nodes; extracting a meta-graph structure by establishing a channel type Hadamard product module, so that multiple interaction conditions existing between nodes at the same time are captured; generating node embedding for the extracted meta-structure containing the meta-path and the meta-graph by using a graph convolutional network; and embedding and mining a downstream text by using the obtained nodes. The method can be suitable for a complex text recognition environment, loss of semantic information is effectively avoided, and rich and complete semantic information can be obtained.

Description

technical field [0001] The invention belongs to the technical field of text mining, in particular to a text mining method for heterogeneous graph conversion based on meta-structure learning. Background technique [0002] With the development of Internet technology, global informatization data presents the characteristics of explosive growth, mass accumulation, and rapid dissemination. We have entered a "big data era", which has had a major impact on cultural communication, information management, etc. Natural language Processing technology has received more and more attention and has become a current hot spot. Natural language processing refers to allowing computers to accept user input in the form of natural language, and internally perform a series of operations such as processing and calculation through algorithms defined by humans, so as to simulate human understanding of natural language and return the results expected by users. The purpose of natural language processi...

AI Technical Summary

Problems solved by technology

Due to the limitations of human experience, manually constructed feature engineering can only capture part of the information

And the meta-path considers each type of relationship between nodes separately. However, the real relationship in a heterogeneous environment is often quite complex, and there may be different types of relationships between nodes, and the meta-path cannot represent two semantic relationships that work at the same time. , which further leads to the loss of semantic information

In traditional text classification methods, limited by word order and lack of text information types, it is difficult to mine information-rich semantic patterns

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