Sentence Semantic Similarity Model Using Convolutional Neural Networks

Authors

  • Karthiga M Bannari Amman Institute of Technology image/svg+xml
  • Sountharrajan S VIT Bhopal University
  • Suganya E Anna University
  • Sankarananth S Excel College of Engineering and Technology

DOI:

https://doi.org/10.4108/eai.25-1-2021.168226

Keywords:

Double sequence, deep learning, convolution neural network, Semantic similarity

Abstract

In Natural Language Processing, determining the semantic likeness between sentences is an important research area. For example, there exists many possible semantics for a word (polysemy), and the synonym of the word differs. Double LSTM (Long Short Term Memory) working at same time on double phrase sequences model is projected to overcome the solitary sequence problem. Furthermore, with the goal of overcoming the second issue, as indicated by the qualities of English dialect, we utilized the British corpus semantic similarity datasets structured by specialists to prepare, and validate the technique. During the training process the stopwords were reserved for use. Convolution Neural Network and Semantic Likeness model based on grammar are used to compare the results of our projected representation. The outcomes demonstrate that the proposed methodology is more prominent than the previous approaches by means of precision, recall rate, accuracy etc., along with the enhanced generalization potential of the neural network.

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Published

25-01-2021

How to Cite

1.
M K, S S, E S, S S. Sentence Semantic Similarity Model Using Convolutional Neural Networks. EAI Endorsed Trans Energy Web [Internet]. 2021 Jan. 25 [cited 2024 May 20];8(35):e8. Available from: https://publications.eai.eu/index.php/ew/article/view/776