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We show that the $\ell_2$ norm of a static sense embedding encodesinformation related to the frequency of that sense in the training corpus usedto learn the sense embeddings. This finding can be seen as an extension of apreviously known relationship for word embeddings to sense embeddings. Ourexperimental results show that, in spite of its simplicity, the $\ell_2$ normof sense embeddings is a surprisingly effective feature for several word senserelated tasks such as (a) most frequent sense prediction, (b) Word-in-Context(WiC), and (c) Word Sense Disambiguation (WSD). In particular, by simplyincluding 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 senseembeddings.

On the Curious Case of $\ell_2$ norm of Sense Embeddings