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Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

Conference Paper


Abstract


  • Micro learning aims to assist users in making

    good use of smaller chunks of spare time and provides an

    effective online learning service. However, to provide such

    personalized online services on the Web, a number of

    information overload challenges persist. Effectively and

    precisely mining and extracting valuable information from

    massive and redundant information is a significant preprocessing

    procedure for personalizing online services. In this

    study, we propose a deep sequence labelling model for locating,

    extracting, and classifying key information for micro learning

    services. The proposed model is general and combines the

    advantages of different types of classical neural network. Early

    evidence shows that it has satisfactory performance compared

    to conventional information extraction methods such as

    conditional random field and bi-directional recurrent neural

    network, for micro learning services.

UOW Authors


  •   Lin, Jiayin (external author)
  •   Zhou, Zhexuan (external author)
  •   Sun, Geng (external author)
  •   Shen, Jun
  •   Pritchard, David (external author)
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Li, Li (external author)
  •   Beydoun, Ghassan

Publication Date


  • 2020

Citation


  • Lin, J., Zhou, Z., Sun, G., Shen, J., Pritchard, D., Cui, T., Xu, D., Li, L. & Beydoun, G. (2020). Deep Sequence Labelling Model for Information Extraction in Micro Learning Service. IEEE International Joint Conference on Neural Networks (pp. 1-10). United States: IEEE.

Start Page


  • 1

End Page


  • 10

Place Of Publication


  • United States

Abstract


  • Micro learning aims to assist users in making

    good use of smaller chunks of spare time and provides an

    effective online learning service. However, to provide such

    personalized online services on the Web, a number of

    information overload challenges persist. Effectively and

    precisely mining and extracting valuable information from

    massive and redundant information is a significant preprocessing

    procedure for personalizing online services. In this

    study, we propose a deep sequence labelling model for locating,

    extracting, and classifying key information for micro learning

    services. The proposed model is general and combines the

    advantages of different types of classical neural network. Early

    evidence shows that it has satisfactory performance compared

    to conventional information extraction methods such as

    conditional random field and bi-directional recurrent neural

    network, for micro learning services.

UOW Authors


  •   Lin, Jiayin (external author)
  •   Zhou, Zhexuan (external author)
  •   Sun, Geng (external author)
  •   Shen, Jun
  •   Pritchard, David (external author)
  •   Cui, Tingru
  •   Xu, Dongming (external author)
  •   Li, Li (external author)
  •   Beydoun, Ghassan

Publication Date


  • 2020

Citation


  • Lin, J., Zhou, Z., Sun, G., Shen, J., Pritchard, D., Cui, T., Xu, D., Li, L. & Beydoun, G. (2020). Deep Sequence Labelling Model for Information Extraction in Micro Learning Service. IEEE International Joint Conference on Neural Networks (pp. 1-10). United States: IEEE.

Start Page


  • 1

End Page


  • 10

Place Of Publication


  • United States