|Table of Contents|

Deep Document Clustering Model Based on Generalization Graph Convolutional Neural Network(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年01期
Page:
82-90
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Deep Document Clustering Model Based on Generalization Graph Convolutional Neural Network
Author(s):
Chai Bianfang1; Li Zheng1; Zhao Xiaopeng2; Wang Rongjuan3
(1.College of Information Engineering,Hebei GEO University,Shijiazhuang 050031,China)
(2.Integrated system operation and maintenance center,Hebei Provincial Department of Finance,Shijiazhuang 050091,China)
(3.Hebei Vocational College of Geology,Shijiazhuang 050086,China)
Keywords:
graph neural network; deep graph clustering; text classification; text representation
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2024.01.010
Abstract:
Text classification is an important task in natural language processing. The method of text classification on graph neural network has become a mainstream method since it can model the interactions among texts. However,most of the existing graph-based classification methods rely on real labels,which are difficult to captain. A deep document clustering model based on graph generalization convolutional neural network(GGCN-DDC)is proposed,which can realize unsupervised text classification while learning text representation. Firstly,the documents are modeled as a text graph. Then generalized convolution layer is used to learn the more distinguishable feature representations of words and the document representations. Finally,The learning algorithm of parameters is constrained by document clustering and reconstructing document graph. Experiments on three benchmark datasets show that GGCN-DDC outperforms other benchmark algorithms on several measures.

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Last Update: 2024-03-15