Abstract
The traditional English part-of-speech analysis model fails to meet people’s actual needs due to the fact that the accuracy and other parameters are not up to standard. Facing large-scale English text data, quickly and accurately obtaining the key information needed and improving the efficiency and accuracy of clustering have always been the focus of attention. However, the inherent characteristics of English text make it impossible to accurately calculate the traditional feature weight calculation method, and its part of speech is difficult to recognize. Moreover, in order to obtain a structure closer to the real data, this paper fuses the norm graph and the k-nearest neighbor graph, proposes a new composition framework, and combines it with two common propagation algorithms to complete the classification task. In addition, in order to obtain the improvement effect of the algorithm, the algorithm is tested on the English text classification corpus data set of the natural language processing open platform, and a control experiment is set to analyze the model performance. Finally, this article combines mathematical statistics to process data and draw corresponding charts.
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Yang, L. Unsupervised machine learning and image recognition model application in English part-of-speech feature learning under the open platform environment. Soft Comput 27, 10013–10023 (2023). https://doi.org/10.1007/s00500-023-08206-9
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DOI: https://doi.org/10.1007/s00500-023-08206-9