A neural network text classification method based on a multi-knowledge map

Abstract

The invention relates to a neural network text classification method based on a multi-knowledge map. The method comprises the following steps: inputting a text in a training set into a long-term and short-term memory network to obtain a context vector of the text; extracting entities from each text in the training set, and performing entity matching in the knowledge map; calculating the attentionweights of matched entities and relationships in the knowledge map under context vector to obtain the overall entity vector, the overall relationship vector and the fact triple vector of the text; computing the fact triple vectors under different knowledge maps, calculating the attention weights of these fact triples, obtaining the text representation vectors and inputting them to the full connection layer of the neural network, and using a classifier to calculate the probability of each text belonging to different categories to train the network; using the trained deep neural network model to predict the category of the text to be predicted. This method improves the model's understanding of text semantics and can classify text content more reliably, accurately and robustly.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and data mining, in particular to a neural network text classification method that integrates multiple knowledge graphs. Background technique [0002] Text classification (text categorization) technology is an important basis for information retrieval and text mining, and its main task is to determine its category according to the text content under the pre-given set of category labels (label). Text classification has a wide range of applications in natural language processing and understanding, information organization and management, content information filtering and other fields. In recent years, the research idea of ​​using deep learning to build a language model has gradually matured, which has greatly improved the feature quality of the text. Some scholars first proposed a sentence classification model based on convolutional neural network, which extracts features from ...

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Problems solved by technology

[0003] At present, there are relatively few studies on enhancing the semantic modeling of deep neural networks with the help of knowledge graphs, and these studies are still relatively rough in combining and matching the information in the knowledge graphs with the text of the training set, which may easily lead to the introduction of too much content related to the text content of the training set. Irrelevant knowledge map information, thus forming noise and affecting modeling

In addition, most of the current research work only considers modeling on a single knowledge graph, which may not be able to cover more of the content of the training set text, and the information between different knowledge graphs can complement each other. Compared with a single knowledge graph, multi-knowledge Atlas can cover more training set text content

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