Dutch Word Sense Disambiguation: Optimizing the Localness of Context

We describe a new version of the Dutch word sense disambiguation system trained and tested on a corrected version of the SENSEVAL-2 data. The system is an ensemble of word experts; each word expert is a memory-based classifier of which the parameters are automatically determined through cross-validation on training material. The original best-performing system, which used only local context features for disambiguation, is further refined by performing additional parallel crossvalidation experiments for optimizing algorithmic parameters and the amount of local context available to each of the word experts’ memory-based kernels. This procedure produces an accuracy of 84.8% on test material, improving on a baseline score of 77.2% and the previous SENSEVAL-2 score of 84.2%. We show that cross-validation overfits; had the local context been held constant at two left and right neighbouring words, the system would have scored 85.0%.