Semantic similarity measures have focused on individual word senses. However, in many ap- plications, it may be informative to compare the overall sense distributions for two differ- ent contexts. We propose a new method for comparing two probability distributions over WordNet, which captures in a single measure the aggregate semantic distance of the com- ponent nodes, weighted by their probability. Previous such measures compute only the dis- tributional distance, and do not take into ac- count the semantic similarity between Word- Net senses across the distributions. To in- corporate semantic similarity, we calculate the (dis)similarity between two probability distri- butions as a weighted distance “travelled” from one to the other through the WordNet hierar- chy. We evaluate the mea...

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