Optimal ensembling (OE) is a word sense disambiguation (WSD) method using word-specific training factors (average positive vs negative training per sense, posex and negex) to predict best system (classifier algorithm / applicable feature set) for given target word. Our official entry (OE1) in Senseval-4 Task 17 (coarse-grained English lexical sample task) contained many design flaws and thus fa...

We compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-...