Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 7, Issue 2


Published
on

July 15, 2022


Pages

707-714


DOI

Article

Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features

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Authors

Guiquan Shen Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Xiaoqing Xiao Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Bojian Wen Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Junzhen Pan Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Wuqiang Shen Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Zhenyue Long Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Jieliang Liang Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
, Yi Wang Affiliation:
Guangdong Power Grid Corporation, Guangzhou, 510000, China
and Moaiad Ahmad Khder Affiliation:
College of Arts & Science, Applied Science University, Bahrain


Abstract

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.


Keywords

Strong Classification Feature Algorithm, Machine Learning, Text Similarity, Semantic Recognition, Performance Evaluation, 34A34


Citation

Shen, G., Xiao, X., Wen, B., Pan, J., Shen, W., Long, Z., Liang, J., Wang, Y., & Khder, M. A. (2022). Calculation and performance evaluation of text similarity based on strong classification features. Applied Mathematics and Nonlinear Sciences, 7(2), 707–714. https://doi.org/10.2478/amns.2022.2.0057

Published by: Engineering Journals

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