Bidirectional LSTM Joint Model for Intent Classification and Named Entity Recognition in Natural Language Understanding

  • Akson Sam VargheseEmail author
  • Saleha Sarang
  • Vipul Yadav
  • Bharat Karotra
  • Niketa Gandhi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)


The aim of this paper is to present a Simple LSTM - Bidirectional LSTM in a joint model framework, for Intent Classification and Named Entity Recognition (NER) tasks. Both the models are approached as a classification task. This paper discuss the comparison of single models and joint models in the respective tasks, a data augmentation algorithm and how the joint model framework helped in learning a poor performing NER model in by adding learned weights from well performing Intent Classification model in their respective tasks. The experiment in the paper shows that there is approximately 44% improvement in performance of NER model when in joint model compared to when tested as independent model.


LSTM Joint model Bidirectional LSTM Intent Classification Named Entity Recognition Natural Language Understanding 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Akson Sam Varghese
    • 1
    Email author
  • Saleha Sarang
    • 1
  • Vipul Yadav
    • 1
  • Bharat Karotra
    • 1
  • Niketa Gandhi
    • 2
  1. 1.Technology and Research GroupDepasser InfotechMumbaiIndia
  2. 2.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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