Department of Foreign Languages and General Studies, Shenyang Urban Construction University, Shenyang, Liaoning, China.
Comput Intell Neurosci. 2022 Aug 20;2022:2391898. doi: 10.1155/2022/2391898. eCollection 2022.
Traditional methods only consider topic information in English vocabulary information extraction, lose the statistical feature information of the keywords themselves, and easily ignore the semantic information of the words. In order to improve the extraction efficiency of English keyword information, based on the CAD mesh model, this paper adds constraint factors such as vertex neighborhood flatness, vertex degree, side length, and flatness on both sides of the side on the basis of the original QEM quadratic error simplification algorithm, and it incorporates a smoothing effect into the edge folding cost function. Moreover, based on the proposed normal vector-based QEM mesh simplification algorithm, the point selection after the edge folding operation is fixed as the vertices of the original edge, and it is applied to the mesh parameterization. In addition, the algorithm solves the local parameterization problem of partially deleted vertices after the simplification operation of each layer is completed. After the model is constructed, the performance of the model is verified through experiments. The research shows that the English keyword information extraction model constructed in this paper is effective.
传统方法仅考虑英文词汇信息提取中的主题信息,丢失了关键词本身的统计特征信息,容易忽略单词的语义信息。为了提高英语关键词信息的提取效率,本文在 CAD 网格模型的基础上,在原始 QEM 二次误差简化算法的基础上增加了顶点邻域平坦度、顶点度、边长和两侧平坦度等约束因素,并将平滑效果纳入边折叠代价函数中。此外,基于所提出的基于法向量的 QEM 网格简化算法,将边折叠操作后的点选择固定为原始边的顶点,并将其应用于网格参数化。此外,该算法解决了每层简化操作完成后部分删除顶点的局部参数化问题。模型构建完成后,通过实验验证了模型的性能。研究表明,本文构建的英语关键词信息提取模型是有效的。