KSU KDD: Word Sense Induction by Clustering in Topic Space
Table 1: Effect of varying the number of topics K on performance
['K', '10', '50', '200', '400', '500']
Before running our main experiments, we wanted to see how the number of topics K used in the topic model could affect the performance of our system. We found that the V-measure and F-score values increase with increasing K, as more dimensions are added to the topic space, the different senses in this K-dimensional space unfold. This trend stops at a value of K=400 in a sign to the limited vocabulary of the training data. This K value is used in all other experiments.