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

Authors:
Tingting Mu;Xinglong Wang;Jun'ichi Tsujii;Sophia Ananiadou
Affiliations:
University of Manchester;University of Manchester;University of Tokyo;University of Manchester
Venue:
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Year:
2010

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Abstract

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 dictionary-based 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 F1 score.