Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification

Improving Non-Negative Positive-Unlabeled Learning for News Headline Classification

With the development of Internet technology, network platforms have gradually become a tool for people to obtain hot news. How to filter out the current hot news from a large number of news collections and push them to users has important application value. In supervised learning scenarios, each pie...

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Bibliographic Details
Main Authors:	Zhanlin Ji, Chengyuan Du, Jiawen Jiang, Li Zhao, Haiyang Zhang, Ivan Ganchev
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Series:	IEEE Access
Subjects:	Text classification non-negative positive-unlabeled (nnPU) learning focal loss virtual adversarial training (VAT) adversarial training for large neural language models (ALUM)
Online Access:	https://ieeexplore.ieee.org/document/10106254/

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