Semi-Supervised Text Classification Method Based on Multi-Granularity Semantic Margin Loss

Semi-supervised text classification uses a limited set of labeled data in conjunction with a large corpus of unlabeled data to develop text classification models. Current pseudo-labeling techniques often face challenges such as pseudo-label bias stemming from decision boundary underfitting and error...

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Autor principal:	LIAO Guoan, XU Ji, CHEN Yanping
Format:	Artigo
Idioma:	Inglês
Publicat:	Editorial Office of Computer Engineering 2026-07-01
Col·lecció:	Jisuanji gongcheng
Matèries:	text classification|pseudo-label|margin loss|feature extraction|decision boundary|semi-supervised learning|natural language processing
Accés en línia:	https://www.ecice06.com/fileup/1000-3428/PDF/20260708.pdf
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