Sentence Semantic Similarity Model Using Convolutional Neural Networks
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 do...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2021-09-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.25-1-2021.168226 |
| _version_ | 1818887964788260864 |
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| author | Karthiga M Sountharrajan S Suganya E Sankarananth S |
| author_facet | Karthiga M Sountharrajan S Suganya E Sankarananth S |
| author_sort | Karthiga M |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T16:45:36Z |
| format | Article |
| id | doaj.art-f37a5a21c6014298908ceb031d362374 |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-12-19T16:45:36Z |
| publishDate | 2021-09-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-f37a5a21c6014298908ceb031d3623742022-12-21T20:13:39ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2021-09-0183510.4108/eai.25-1-2021.168226Sentence Semantic Similarity Model Using Convolutional Neural NetworksKarthiga M0Sountharrajan S1Suganya E2Sankarananth S3Bannari Amman Institute of Technology, Tamilnadu, IndiaVIT Bhopal University, Madhya Pradesh, IndiaAnna University, Tamilnadu, IndiaExcel College of Engineering and Technology, Tamilnadu, IndiaIn 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.https://eudl.eu/pdf/10.4108/eai.25-1-2021.168226double sequencedeep learningconvolution neural networksemantic similarity |
| spellingShingle | Karthiga M Sountharrajan S Suganya E Sankarananth S Sentence Semantic Similarity Model Using Convolutional Neural Networks EAI Endorsed Transactions on Energy Web double sequence deep learning convolution neural network semantic similarity |
| title | Sentence Semantic Similarity Model Using Convolutional Neural Networks |
| title_full | Sentence Semantic Similarity Model Using Convolutional Neural Networks |
| title_fullStr | Sentence Semantic Similarity Model Using Convolutional Neural Networks |
| title_full_unstemmed | Sentence Semantic Similarity Model Using Convolutional Neural Networks |
| title_short | Sentence Semantic Similarity Model Using Convolutional Neural Networks |
| title_sort | sentence semantic similarity model using convolutional neural networks |
| topic | double sequence deep learning convolution neural network semantic similarity |
| url | https://eudl.eu/pdf/10.4108/eai.25-1-2021.168226 |
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