Sentence Semantic Similarity Model Using Convolutional Neural Networks

M, Karthiga and S, Sountharrajan and E, Suganya and S, Sankarananth (2021) Sentence Semantic Similarity Model Using Convolutional Neural Networks. EAI Endorsed Transactions on Energy Web. e16. ISSN 2032-944X

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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.

Item Type: Article
Uncontrolled Keywords: Double sequence, deep learning, convolution neural network, Semantic similarity
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 20 Jul 2021 09:52
Last Modified: 20 Jul 2021 09:52
URI: https://eprints.eudl.eu/id/eprint/4905

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