Using pseudowords we can overcome data sparseness problem in supervised WSD and fully verify the experimental effect of word sense classifier.
使用伪词可以避免有指导的词义消歧方法中的数据稀疏问题，充分验证词义分类器的实验效果。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
然而，传统的监督学习算法需要标记大量的训练样本来建立满意的分类器。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.
半分类学习-将标签与非标签用例劫后生成一个合适的函数或分类器。
Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.
半分类学习-将标签与非标签用例劫后生成一个合适的函数或分类器。
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