Imbalanced classification using dictionary-based prototypes and hierarchical decision rules for entity sense disambiguation

Entity sense disambiguation becomes difficult with few or even zero training instances available, which is known as imbalanced learning problem in machine learning. To overcome the problem, we create a new set of reliable training instances from dictionary, called dictionarybased prototypes. A hierarchical classification system with a tree-like structure is designed to learn from both the prototypes and training instances, and three different types of classifiers are employed. In addition, supervised dimensionality reduction is conducted in a similarity-based space. Experimental results show our system outperforms three baseline systems by at least 8.3% as measured by macro F 1 score.