Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem

In this paper, we analyze the effect of resampling techniques, including undersampling and over-sampling used in active learning for word sense disambiguation (WSD). Experimental results show that under-sampling causes negative effects on active learning, but over-sampling is a relatively good choice. To alleviate the withinclass imbalance problem of over-sampling, we propose a bootstrap-based oversampling (BootOS) method that works better than ordinary over-sampling in active learning for WSD. Finally, we investigate when to stop active learning, and adopt two strategies, max-confidence and min-error, as stopping conditions for active learning. According to experimental results, we suggest a prediction solution by considering max-confidence as the upper bound and min-error as the lower bound for stopping conditions.

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