Instead of deriving the clustering tree and guessing at a perfect cut, we stop the clustering algorithm when Z clusters have been created, where Z is the number of senses in which the occurrences of TW i have been used in the text in question.
considering the fine granularity of the PWN2.0 sense inventory, our disambiguation results using parallel resources are superior to the state of the art in monolingual WSD (with the same sense inventory).