Semantic Marginal Autoencoder Model for the Word Embedding Technique for the Marginal Denoising in the Different Languages

Authors

  • Deepak Kumar Assistant Professor, Department of Humanities and Social Sciences, Maulana Azad National Institute of Technology, Bhopal, India. https://orcid.org/0000-0002-7312-8561
  • L. Vertivendan Assistant Professor, Department of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India. https://orcid.org/0000-0001-5499-7019
  • K. Velmurugan Assistant Professor, Department of English, Anurag University, Telangana, India. https://orcid.org/0000-0003-4067-6745
  • Kumarasamy M. Assistant Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia
  • Dhanashree Toradmalle Associate Professor, Department of Computer Engineering, K J Somaiya Institute of Technology, India.
  • Khan Vajid Nabilal Associate Professor, Computer Engineering, KJ College of Engineering and Management Research, Maharashtra, India. https://orcid.org/0000-0002-0999-9776

Keywords:

Semantics, Word Embedding, Marginal Estimation, Neighbourhood Estimation, Accuracy

Abstract

The words are comprised of the smaller elements for the practical evaluation of the languages for the election of effective sematic. The conventional semantic technique subjected to the challenges associated with the incorporation of the different feature variables for the computation. However, the word embedding technique is complex due to the presence of the difference in the language features. This paper aimed to develop as an effective semantic model integrated with the Auto Encoder model. The proposed model is termed as Sematic Marginal Auto Encoder (SMarginalAE) for the different language sequences. The proposed model comprises of the Marginal features with the neighborhood estimation of the features. The proposed SMarginalAE achieves the neighborhood accuracy of 92.45% and the pair-wise accuracy is estimated as the 88.94%. The comparative analysis emphasised that the suggested SMarginalAE framework achieves the ~3% enhanced efficiency than the conventional techniques.

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Process in SMarginalAE

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Published

10.02.2023

How to Cite

Kumar, D., Vertivendan, L. ., Velmurugan, K. ., M., K. ., Toradmalle, D. ., & Vajid Nabilal, K. . (2023). Semantic Marginal Autoencoder Model for the Word Embedding Technique for the Marginal Denoising in the Different Languages. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 204–210. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2562

Issue

Vol. 11 No. 3s (2023)

Section

Research Article

License

Copyright (c) 2023 Deepak Kumar, L. Vertivendan, K. Velmurugan, Kumarasamy M., Dhanashree Toradmalle, Khan Vajid Nabilal

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