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Linked Document Classification by Network Representation Learning

  • Yue Zhang
  • Liying Zhang
  • Yao Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

Network Representation Learning (NRL) can learn a latent space representation of each vertex in a topology network structure to reflect linked information. Recently, NRL algorithms have been applied to obtain document embedding in linked document network, such as citation websites. However, most existing document representation methods with NRL are unsupervised and they cannot combine NRL with a concrete task-specific NLP tasks. So in this paper, we propose a unified end-to-end hybrid Linked Document Classification (LDC) model which can capture semantic features and topological structure of documents to improve the performance of document classification. In addition, we investigate to use a more flexible strategy to capture structure similarity to improve the traditional rigid extraction of linked document topology structure. The experimental results suggest that our proposed model outperforms other document classification methods especially in the case of having less training sets.

Keywords

Document classification NRL Flexible random walk strategy 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Scientific and Technical Information of ChinaBeijingChina
  2. 2.School of Software and MicroelectronicsPeking UniversityBeijingChina

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