Imbalanced Classification Using Dictionary-based Prototypes and Hierarchical Decision Rules for Entity Sense Disambiguation
Tingting Mu, Xinglong Wang, Jun’ichi Tsujii, Sophia Ananiadou · 2010
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.