Text knowledge supplementing method and device based on knowledge graph

Abstract

The embodiment of the invention provides a text knowledge supplementing method and device based on a knowledge graph, and the method comprises the steps: obtaining and splicing a character vector, a word vector and a subject vector of a text, and inputting the character vector, the word vector and the subject vector into a bidirectional GRU to obtain a hidden state; processing the hidden state by using a self-attention mechanism to obtain a feature matrix, and converting the feature matrix into a feature vector through a pooling layer; calling a knowledge graph to carry out conceptualization processing on the text to obtain a concept set comprising concept vectors; calculating a relation weight between the concept vector and the feature vector; calculating the importance weight of the concept vector in the concept set; and adjusting the corresponding relation weights by using the importance weights, and performing weighted calculation on each concept vector according to the adjusted relation weights to obtain concept set features so as to perform knowledge supplement on the text through the concept set features. According to the method, the text features are expanded from the aspects of character granularity, word granularity and text granularity, and the deficiency of text context information is made up by means of the knowledge graph.

Description

technical field [0001] The present application relates to the field of natural language processing, in particular to a method and device for supplementing text knowledge based on knowledge graphs. Background technique [0002] Text classification is a widely used natural language processing technique. Using text classification, you can quickly mine the hidden information in the massive texts on the Internet, and it is widely used in information retrieval, question answering systems, dialogue systems and other fields. [0003] Due to the short length of text in the network, lack of context, and imprecise expression, there are problems such as difficulty in semantic understanding and sparse features in text classification processing. The use of knowledge graphs can effectively alleviate the feature sparsity problem of texts. However, in the text knowledge supplement method that introduces external knowledge, there is still the problem of low quality of the introduced knowledg...

AI Technical Summary

Problems solved by technology

[0003] Due to the short length of text in the network, lack of context, and imprecise expression, there are problems such as semantic understanding difficulties and sparse features in text classification processing.

The feature sparsity problem of text can be effectively alleviated by using the knowledge graph. However, in the text knowledge supplement method that introduces external knowledge, there is still the problem of low quality of the introduced knowledge, which leads to the lack of context information in the text and poor semantic understanding.

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