Clustering and Analysis of Verbs in English Language Based on Artificial Intelligence

Yanyang Zheng, Zongjie Zhang and Zaoli Yang

Mathematical Problems in Engineering, 2022, vol. 2022, 1-10

Abstract: There are related problems in the clustering and analysis of verbs in the English language. In order to further improve the application of verbs in the English language based on the theory of artificial intelligence, the clustering analysis of English verbs is carried out by using artificial intelligence technology and through the use of discrete matrix and evaluation index optimization, so as to obtain the optimized artificial intelligence model. The model can provide a good computational process for the verb clustering analysis of the English language, and the experimental data are used to verify the model. Relevant studies show that the influence of parameter change on the discrete change of the matrix can be divided into three cases: the curve shows a gentle change trend with the increase of distance; the increase of the distance makes the discrete data of the experiment increase approximately linear. The relationship between distance and discrete data is nonlinear. The change curve of MS has obvious multi-segment linear characteristics, and the linear slope of the first stage is the lowest, indicating that the change effect of the curve is the least obvious in this stage. Based on the clustering analysis of English language verbs using artificial intelligence technology, it can be seen that different factors vary in an obvious range at the initial stage and have good corresponding fluctuation characteristics. With the increase of samples, the corresponding curve gradually tends to be flat. Finally, the accuracy of the model is verified by experimental data. This research can provide a theoretical basis for the application and analysis of artificial intelligence in other fields and provide support for clustering and analysis of verbs in the English language.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2151598

DOI: 10.1155/2022/2151598

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