ew 21(35): e8

Research Article

Sentence Semantic Similarity Model Using Convolutional Neural Networks

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  • @ARTICLE{10.4108/eai.25-1-2021.168226,
        author={Karthiga M and Sountharrajan S and Suganya E and Sankarananth S},
        title={Sentence Semantic Similarity Model Using Convolutional Neural Networks},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={8},
        number={35},
        publisher={EAI},
        journal_a={EW},
        year={2021},
        month={1},
        keywords={Double sequence, deep learning, convolution neural network, Semantic similarity},
        doi={10.4108/eai.25-1-2021.168226}
    }
    
  • Karthiga M
    Sountharrajan S
    Suganya E
    Sankarananth S
    Year: 2021
    Sentence Semantic Similarity Model Using Convolutional Neural Networks
    EW
    EAI
    DOI: 10.4108/eai.25-1-2021.168226
Karthiga M1,*, Sountharrajan S2, Suganya E3, Sankarananth S4
  • 1: Bannari Amman Institute of Technology, Tamilnadu, India
  • 2: VIT Bhopal University, Madhya Pradesh, India
  • 3: Anna University, Tamilnadu, India
  • 4: Excel College of Engineering and Technology, Tamilnadu, India
*Contact email: karthigam@bitsathy.ac.in

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.