Local ensemble learning from imbalanced and noisy data for word sense disambiguation.
Pattern Recognition(2018)
摘要
•A study of effects of class imbalance and label noise on word sense disambiguation.•Local ensemble learning robust to class skewness and corrupted training labels.•Random subspaces and kernel whitening for handling high-dimensional data.•Two-level fusion for efficient usage of one-class classifiers for multi-class tasks.•Thorough experimental study on challenging real-life word sense disambiguation data.
更多查看译文
关键词
Machine learning,Natural language processing,Imbalanced classification,Multi-class imbalance,Ensemble learning,One-class classification,Class label noise,Word sense disambiguation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络