Nevertheless, when |D| is large (e.g., > 100), the performance of classification may decrease, especially when the distribution of d in training dataset differs from that in the test set.
when |D| is large, named entity disambiguation becomes a multi-class classification task on heterogeneous and imbalanced datasets, which is challenging for a machine learning model to learn to discriminate enough between the semantic classes (Japkowicz, 2000).