On the other hand, if we are not confident about the supervised KPCA model's prediction, we then turn to the semi-supervised KPCA model and take its classification as the predicted sense.
the composite model uses the following algorithm to combine the sense predictions of the supervised and semi-supervised KPCA models in order to disambiguate the target word in a given test instance x: 1. let s 1 be the predicted sense of x using the supervised KPCA baseline model 2. let c be the similarity between x and its most similar training instance 3. if c ≥ t or s 1 = s mf (where t is a preset threshold, and s mf is the most frequent sense of the target word): • then predict the sense of the target word of x to be s 1 • else predict the sense of the target word of x to be s 2 , the sense predicted by the semisupervised KPCA model The two conditions checked in step 3 serve to filter those instances where the supervised KPCA baseline model is confident enough to skip the semi-supervised KPCA model.