Structure of the RoBERTa module.

<p>Input text is first processed through word and positional embeddings to generate context-aware vector representations. After being processed by the multi-head self-attention mechanism, the model gradually produces emotion features containing global contextual information, with the final glo...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jiangting Yan (20818388) (author)
مؤلفون آخرون:	Pengju Pu (20818391) (author), Liheng Jiang (20818394) (author)
منشور في:	2025
الموضوعات:	Sociology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified incorporating temporal dynamics improving computational efficiency effective feature extraction demonstrating significant improvements deep learning baselines conditional random fields challenging task due model &# 8217 large labeled datasets graph neural networks rgc net provides rgc net achieves emotion label prediction capture emotion evolution rgc net used datasets social networks proposed model propose emotion emotion recognition emotion classification unstructured text unstructured nature two widely strong performance social media robust solution results indicate novel approach including reducing identify areas gnn captures gnn ), generated content future research
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Structure of the RoBERTa module.

مواد مشابهة