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Tag Prediction in Social Annotation Systems Based on CNN and BiLSTM

  • Baiwei LiEmail author
  • Qingchuan Wang
  • Xiaoru Wang
  • Wei Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

Social annotation systems enable users to annotate large-scale texts with tags which provide a convenient way to discover, share and organize rich information. However, manually annotating massive texts is in general costly in manpower. Therefore, automatic annotation by tag prediction is of great help to improve the efficiency of semantic identification of social contents. In this paper, we propose a tag prediction model based on convolutional neural networks (CNN) and bi-directional long short term memory (BiLSTM) network, through which, tags of texts can be predicted efficiently and accurately. By Experiments on real-world datasets from a social Q&A community, the results show that the proposed CNN-BiLSTM model achieves state-of-the-art accuracy for tag prediction.

Keywords

Tag prediction Convolutional neural network Prediction Bi-directional LSTM Deep learning 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) grant funded by the China government, Ministry of Science and Technology(No.61672108).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Beijing Information Science and Technology UniversityBeijingChina
  3. 3.Beijing University of TechnologyBeijingChina

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