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Radical Enhanced Chinese Word Embedding Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Linked Document Classification by Network Representation Learning

Authors : Yue Zhang, Liying Zhang, Yao Liu

Published in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Publisher: Springer International Publishing

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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.

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previous chapter TSABCNN: Two-Stage Attention-Based Convolutional Neural Network for Frame Identification
next chapter A Word Embedding Transfer Model for Robust Text Categorization
Footnotes
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Metadata
Title
Linked Document Classification by Network Representation Learning
Authors
Yue Zhang
Liying Zhang
Yao Liu
Copyright Year
2018
Book
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_25

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