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In this paper, we evaluate the results of the Antwerp University word sense disambiguation system in the English all words task of SENSEVAL-2. In this approach, specialized memory-based word-experts were trained per word-POS combination. Through optimization by cross-validation of the individual component classifiers and the voting scheme for combining them, the best possible word-expert was determined. In the competition, this word-expert architecture resulted in accuracies of 63.6% (fine-grained) and 64.5% (coarse-grained) on the SENSEVAL-2 test data.In order to better understand these results, we investigated whether classifiers trained on different information sources performed differently on the different part-of-speech categories. Furthermore, the results were evaluated in terms of the available number of training items, the number of senses, and the sense distributions in the data set. We conclude that there is no information source which is optimal over all word-experts. Selecting the optimal classifier/voter for each single word-expert, however, leads to major accuracy improvements. We furthermore show that accuracies do not so much depend on the available number of training items, but largely on polysemy and sense distributions.

Evaluating the results of a memory-based word-expert approach to unrestricted word sense disambiguation