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

A knowledge map and neural network technology, applied in the fields of natural language processing and data mining, can solve problems such as inability to cover more, rough combination of information and training set text, noise, etc.

Active Publication Date: 2018-12-11
FUZHOU UNIV
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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 knowled

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

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Embodiment Construction

[0054] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0055] figure 1 It is an implementation flowchart of a neural network text classification method fused with multi-knowledge graphs in the present invention. Such as figure 1 As shown, the method includes the following steps:

[0056] Step A: Input the text in the training set into the long short-term memory network to obtain the context vector of the text. Specifically include the following steps:

[0057] Step A1: For any text D, perform word segmentation processing, and use the word embedding tool to convert the words in the text into word vector form. The calculation formula is as follows:

[0058] v=W·v'

[0059] Among them, each word in the text is randomly initialized as a d'-dimensional real number vector v'; W is the word embedding matrix, W∈R d ×d′ , which is obtained from a large-scale corpus trained in a neural netwo...

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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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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F18/2413G06F18/24147
Inventor 陈羽中张伟智郭昆林剑
Owner FUZHOU UNIV
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