<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance...

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

Publicado en línea: 15 jul 2022

Páginas: 707 - 714

Recibido: 12 feb 2022

Aceptado: 11 abr 2022

DOI: https://doi.org/10.2478/amns.2022.2.0057

Palabras clave
Strong Classification Feature Algorithm, Machine Learning, Text Similarity, Semantic Recognition, Performance Evaluation

© 2023 Guiquan Shen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publicado en línea: 15 jul 2022

Páginas: 707 - 714

Recibido: 12 feb 2022

Aceptado: 11 abr 2022

DOI: https://doi.org/10.2478/amns.2022.2.0057

Palabras claveStrong Classification Feature Algorithm, Machine Learning, Text Similarity, Semantic Recognition, Performance Evaluation

© 2023 Guiquan Shen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Figure 2

Figure 3

Figure 4

Palabras clave
Strong Classification Feature Algorithm, Machine Learning, Text Similarity, Semantic Recognition, Performance Evaluation