This isn't really WSD though, or at least, only very weakly.
Rare words are usually pretty unambiguous for part-of-speech. I would guess this mostly has an effect on the top 5,000 items of the vocabulary, and most of the rest of the lexicon only has a single "sense".
This isn't really WSD though, or at least, only very weakly.
Sure. I was pointing to real WSD where sparseness becomes even a stronger problem than when your definition of sense is restricted to part-of-speech tag or sentiment.
Rare words are usually pretty unambiguous for part-of-speech.
I was talking about (possibly) frequent words where some part-of-speech are infrequent, not about rare words. To take five more or less random examples form the Brown corpus (yes, we train on large corpora, but I think similar distributions could hold for less frequent forms in languages with e.g. frequent nomalization, not everyone speaks English!):
mother NN 173 VB 1
code NN 20 VB 1
hanging NN 1 VBG 20
level JJ 14 NN 172 VB 2
services NNS 115 VBZ 1
If your learning method is as coarse-grained as simply throwing the token plus part-of-speech into word2vec or wang2vec, some will be below the frequency cut-offs (or will be to sparse to learn good embeddings), while other 'senses' may in reality be semantically similar.
Rare words are usually pretty unambiguous for part-of-speech. I would guess this mostly has an effect on the top 5,000 items of the vocabulary, and most of the rest of the lexicon only has a single "sense".