Quality assessment of collaboratively-created web content with no manual intervention based on soft multi-view generation
作者：
Highlights：
• We propose automatic ways for assessing the quality of collaborative Web content.
• Our solutions exploit relaxed multiview learning (soft views).
• Automatic soft views are created by finding clusters of highly correlated features.
• Experiments on Wiki sets show that our solution reduces classification error by 20.
• Our automatic views are very similar to those manually defined.
摘要
•We propose automatic ways for assessing the quality of collaborative Web content.•Our solutions exploit relaxed multiview learning (soft views).•Automatic soft views are created by finding clusters of highly correlated features.•Experiments on Wiki sets show that our solution reduces classification error by 20.•Our automatic views are very similar to those manually defined.
论文关键词：Multi-view,Machine learning,Information retrieval,Automatic text quality assessment
论文评审过程：Received 23 December 2018, Revised 4 April 2019, Accepted 21 April 2019, Available online 25 April 2019, Version of Record 13 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.053
