Structure of skip-gram model [37].

<div><p>Text classification, as an important research area of text mining, can quickly and effectively extract valuable information to address the challenges of organizing and managing large-scale text data in the era of big data. Currently, the related research on text classification te...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Lu Xiao (314030) (author)
مؤلفون آخرون:	Qiaoxing Li (730432) (author), Qian Ma (69668) (author), Jiasheng Shen (19871113) (author), Yong Yang (63842) (author), Danyang Li (1490554) (author)
منشور في:	2024
الموضوعات:	Genetics Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified public opinion monitoring knn ), known uneven text length 29 %, 5 important research area level tourist attractions text classification tends scale text data class text classification word2vec </ p optimizing traditional tf identify rare categories tourist attractions subcategories specific category performance text classification algorithm tourist attractions text set text mining text datasets category discriminability big data 55 %, six subcategories related research multiple subcategories good performance vector expression useful reference true value specific fields results demonstrate proposed algorithm professional dataset predicted value pos weighting modified tf model stability managing large imbalanced dataset even outperforms efficiently implement different sizes conclusions obtained beneficial exploration algorithm presented achieve multi
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Structure of skip-gram model [37].

مواد مشابهة