Semantic Marginal Autoencoder Model for the Word Embedding Technique for the Marginal Denoising in the Different Languages - Kumar, Deepak; Vertivendan, L.; Velmurugan, K.; M., Kumarasamy; Toradmalle, Dhanashree; Vajid Nabilal, Khan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.