Trajectory based word sense disambiguation

Classifier combination is a promising way to improve performance of word sense disambiguation. We propose a new combinational method in this paper. We first construct a series of Naïve Bayesian classifiers along a sequence of orderly varying sized windows of context, and perform sense selection for both training samples and test samples using these classifiers. We thus get a sense selection trajectory along the sequence of context windows for each sample. Then we make use of these trajectories to make final k-nearest-neighbors-based sense selection for test samples. This method aims to lower the uncertainty brought by classifiers using different context windows and make more robust utilization of context while perform well. Experiments show that our approach outperforms some other algorithms on both robustness and performance