Study on English verb classification based on K-means algorithm and support vector machine algorithm

Shuyi Teng

doi:10.1117/12.3033555

12 July 2024 Study on English verb classification based on K-means algorithm and support vector machine algorithm

Shuyi Teng

Proceedings Volume 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) ; 131850B (2024) https://doi.org/10.1117/12.3033555
Event: International Conference on Communication, Information and Digital Technologies, 2024, Wuhan, China

Abstract

Facing the challenge of English verb inflection classification, a technique combining K-means clustering algorithm with Support Vector Machine (SVM) is proposed to enhance the efficiency and accuracy of classification. This study establishes a semantic structure model of English verb inflection and accurately describes its semantic attributes, aiming to capture the state features. Quantification of similarity between these state feature quantities using cosine similarity evaluation method is performed. Experimental results demonstrate a high level of accuracy in calculating the similarity of state feature quantities, effectively classifying both regular and irregular changes in verb inflection. By setting the positive slack factor and error penalty parameters to 3000 and 9, respectively, further optimization of classification results is achieved, indicating potential application in the field. To ensure scientific rigor and authority, extensive citations from relevant literature are provided, elucidating differences from existing technologies and their advantages. Strict adherence to APA Seventh Edition citation and formatting standards solidifies the paper's academic contribution with a robust literature foundation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuyi Teng "Study on English verb classification based on K-means algorithm and support vector machine algorithm", Proc. SPIE 13185, International Conference on Communication, Information, and Digital Technologies (CIDT2024) , 131850B (12 July 2024); https://doi.org/10.1117/12.3033555

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