Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

We propose, in this article, the Hierarchical Behavior-Knowledge Space as an extension of Behavior-Knowledge Space. Hierarchical BKS utilizes ranked level individual classifiers, and automatically expands its behavioral knowledge in order to satisfy given reliability requirement. From the statistical view point, its decisions are as optimal as those of original BKS, and the reliability threshold is a lower bound of estimated reliability. Several comparisons with original BKS and unanimous voting are shown with some experiments.