This paper describes the Duluth systems that participated in Task 15 of SemEval 2015. The goal of the task was to automatically construct dictionary entries (via a series of three sub-tasks). Our systems participated in subtask 2, which involved automatically clustering the contexts in which a target word occurs into its different senses. Our results are consistent with previous word sense induction and discrimination findings, where it proves difficult to beat a baseline algorithm that assigns all instances of a target word to a single sense. However, our method of predicting the number of senses automatically fared quite well.
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