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ZERO SHOT LEARNING FOR TEXT CLASSIFICATION.

  • Unique Paper ID: 157586
  • Volume: 9
  • Issue: 7
  • PageNo: 601-603
  • Abstract:
  • Zero shot learning aims at solving the problem of object classification where we have no training examples as insufficient and uneven datasets for emerging classes is becoming a challenging issue monotonously. Zero shot text classification is an approach where we use zero shot learning to make models which are capable of categorizing the text documents which it has not seen during the testing phase. In this paper we will be giving a brief idea about zero shot learning and text classification with the different state-of- art methods which has been used for zero shot text classification problem which includes neural network with embeddings, semantic embeddings, using knowledge graphs etc.In addition we have applied distil BERT -a pretrained model, for seen classes and the results for the same has been discussed.
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  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 7
  • PageNo: 601-603

ZERO SHOT LEARNING FOR TEXT CLASSIFICATION.

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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