Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features
Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2022.2.0057 |
| _version_ | 1797688777322266624 |
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| author | Shen Guiquan Xiao Xiaoqing Wen Bojian Pan Junzhen Shen Wuqiang Long Zhenyue Liang Jieliang Wang Yi Khder Moaiad Ahmad |
| author_facet | Shen Guiquan Xiao Xiaoqing Wen Bojian Pan Junzhen Shen Wuqiang Long Zhenyue Liang Jieliang Wang Yi Khder Moaiad Ahmad |
| author_sort | Shen Guiquan |
| collection | DOAJ |
| description | Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition code as a comparison object is designed. It drives the algorithm modules of two fuzzy neuron deep convolution machine learning, and between these two processes of machine learning, a rigid algorithm based on Fourier transform frequency domain feature is extracted. Finally, a more complex machine learning general algorithm is realized by the use of external data fuzzy algorithm and de-fuzzy algorithm before and after the algorithm module. It is also a technical innovation in this paper. Through the performance evaluation based on the subjective evaluation of volunteers, it is found that the system focuses on the text semantic similarity evaluation of the Chinese language, and achieves a comparison result of 81.78% of the artificial judgment accuracy rate, and only 5.52% of the volunteers believe that the system judgment result is completely different from that of manual judgment. |
| first_indexed | 2024-03-12T01:36:39Z |
| format | Article |
| id | doaj.art-e036834e89d64c9e81672a3a9cb4a74e |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-12T01:36:39Z |
| publishDate | 2023-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-e036834e89d64c9e81672a3a9cb4a74e2023-09-11T07:01:08ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562023-01-018170771410.2478/amns.2022.2.0057Calculation and Performance Evaluation of Text Similarity Based on Strong Classification FeaturesShen Guiquan0Xiao Xiaoqing1Wen Bojian2Pan Junzhen3Shen Wuqiang4Long Zhenyue5Liang Jieliang6Wang Yi7Khder Moaiad Ahmad81Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China1Guangdong Power Grid Corporation, Guangzhou, 510000, China2College of Arts & Science, Applied Science University, BahrainBased on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition code as a comparison object is designed. It drives the algorithm modules of two fuzzy neuron deep convolution machine learning, and between these two processes of machine learning, a rigid algorithm based on Fourier transform frequency domain feature is extracted. Finally, a more complex machine learning general algorithm is realized by the use of external data fuzzy algorithm and de-fuzzy algorithm before and after the algorithm module. It is also a technical innovation in this paper. Through the performance evaluation based on the subjective evaluation of volunteers, it is found that the system focuses on the text semantic similarity evaluation of the Chinese language, and achieves a comparison result of 81.78% of the artificial judgment accuracy rate, and only 5.52% of the volunteers believe that the system judgment result is completely different from that of manual judgment.https://doi.org/10.2478/amns.2022.2.0057strong classification feature algorithmmachine learningtext similaritysemantic recognitionperformance evaluation34a34 |
| spellingShingle | Shen Guiquan Xiao Xiaoqing Wen Bojian Pan Junzhen Shen Wuqiang Long Zhenyue Liang Jieliang Wang Yi Khder Moaiad Ahmad Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features Applied Mathematics and Nonlinear Sciences strong classification feature algorithm machine learning text similarity semantic recognition performance evaluation 34a34 |
| title | Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features |
| title_full | Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features |
| title_fullStr | Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features |
| title_full_unstemmed | Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features |
| title_short | Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features |
| title_sort | calculation and performance evaluation of text similarity based on strong classification features |
| topic | strong classification feature algorithm machine learning text similarity semantic recognition performance evaluation 34a34 |
| url | https://doi.org/10.2478/amns.2022.2.0057 |
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