  We show that the $\ell_2$ norm of a static sense embedding encodes
information related to the frequency of that sense in the training corpus used
to learn the sense embeddings. This finding can be seen as an extension of a
previously known relationship for word embeddings to sense embeddings. Our
experimental results show that, in spite of its simplicity, the $\ell_2$ norm
of sense embeddings is a surprisingly effective feature for several word sense
related tasks such as (a) most frequent sense prediction, (b) Word-in-Context
(WiC), and (c) Word Sense Disambiguation (WSD). In particular, by simply
including the $\ell_2$ norm of a sense embedding as a feature in a classifier,
we show that we can improve WiC and WSD methods that use static sense
embeddings.
